# Evolution of continental temperature seasonality from the Eocene greenhouse to the Oligocene icehouse –a model–data comparison

Agathe Toumoulin, Delphine Tardif, Yannick Donnadieu, Alexis Licht, Jean-Baptiste Ladant, Lutz Kunzmann, Guillaume Dupont-Nivet

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## To cite this version:

Agathe Toumoulin, Delphine Tardif, Yannick Donnadieu, Alexis Licht, Jean-Baptiste Ladant, et al. Evolution of continental temperature seasonality from the Eocene greenhouse to the Oligocene icehouse –a model–data comparison. Climate of the Past, 2022, 18 (2), pp.341 - 362. 10.5194/cp-18-341-2022. insu-03592787

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# Evolution of continental temperature seasonality from the Eocene greenhouse to the Oligocene icehouse – a model–data comparison

Agathe Toumoulin<fnref n="1" />, Delphine Tardif<fnref n="1" />,<fnref n="2" />, Yannick Donnadieu<fnref n="1" />, Alexis Licht<fnref n="1" />, Jean-Baptiste Ladant<fnref n="3" />, Lutz Kunzmann<fnref n="4" />, and Guillaume Dupont-Nivet<fnref n="5" />,<fnref n="6" />

**Correspondence:** Agathe Toumoulin (agathe.toumoulin@gmail.com)

Received: 10 March 2021 – Discussion started: 1 April 2021

Revised: 13 January 2022 – Accepted: 13 January 2022 – Published: 28 February 2022

**Abstract.** At the junction of greenhouse and icehouse cli-
mate states, the Eocene–Oligocene Transition (EOT) is a
key moment in Cenozoic climate history. While it is asso-
ciated with severe extinctions and biodiversity turnovers on
land, the role of terrestrial climate evolution remains poorly
resolved, especially the associated changes in seasonality.
Some paleobotanical and geochemical continental records in
parts of the Northern Hemisphere suggest the EOT is asso-
ciated with a marked cooling in winter, leading to the de-
velopment of more pronounced seasons (i.e., an increase in
the mean annual range of temperature, MATR). However,
the MATR increase has been barely studied by climate mod-
els and large uncertainties remain on its origin, geographi-
cal extent and impact. In order to better understand and de-
scribe temperature seasonality changes between the middle
Eocene and the early Oligocene, we use the Earth system
model IPSL-CM5A2 and a set of simulations reconstruct-
ing the EOT through three major climate forcings: pCO₂
decrease (1120, 840 and 560 ppm), the Antarctic ice-sheet
(AIS) formation and the associated sea-level decrease. Our
simulations suggest that pCO₂ lowering alone is not suffi-
cient to explain the seasonality evolution described by the
data through the EOT but rather that the combined effects of
pCO₂, AIS formation and increased continentality provide

the best data–model agreement. pCO₂ decrease induces a
zonal pattern with alternating increasing and decreasing sea-
sonality bands particularly strong in the northern high lati-
tudes (up to 8 °C MATR increase) due to sea-ice and surface
albedo feedback. Conversely, the onset of the AIS is respon-
sible for a more constant surface albedo yearly, which leads
to a strong decrease in seasonality in the southern midlati-
tudes to high latitudes (> 40° S). Finally, continental areas
that emerged due to the sea-level lowering cause the largest
increase in seasonality and explain most of the global hetero-
geneity in MATR changes (ΔMATR) patterns. The ΔMATR
patterns we reconstruct are generally consistent with the vari-
ability of the EOT biotic crisis intensity across the Northern
Hemisphere and provide insights on their underlying mecha-
nisms.

# 1 Introduction

## 1.1 Context and aim of the study

The Eocene–Oligocene Transition (EOT) is marked by
an abrupt cooling event (~2.9 °C from marine proxies;
Hutchinson et al., 2021), regarded as the hinge between
the Eocene greenhouse and the later Cenozoic icehouse.

<footnote n="1">¹Aix Marseille Université, CNRS, IRD, INRA, Collège de France, CEREGE, 13545 Aix-en-Provence, France</footnote>

<footnote n="2">²Institut de physique du globe de Paris, Université de Paris, CNRS, 75005 Paris, France</footnote>

<footnote n="3">³Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France</footnote>

<footnote n="4">⁴Senckenberg Natural History Collections Dresden, 01109 Dresden, Germany</footnote>

<footnote n="5">⁵Géosciences Rennes, UMR CNRS 6118, Université de Rennes, 35042 Rennes, France</footnote>

<footnote n="6">⁶Institute of Geosciences, Potsdam University, 14469 Potsdam, Germany</footnote>
This event is associated with the first major expansion of the Antarctic ice sheet with an estimated sea-level drop of ~70 m (Hutchinson et al., 2021; Miller et al., 2020). The EOT is described as a relatively brief event (~790 000 years), with two successive steps (at ca. 33.9 and 33.7) recognized in extensively studied marine environments, especially from deep ocean δ¹⁸O values (e.g., Katz et al., 2008; Zachos et al., 2001; see the review of Hutchinson et al., 2021).

Reported vegetation responses to the EOT appear to be heterogeneous across continents with important composition changes in some areas (e.g., the west coast of the United States, Greenland, Asia), notably where faunal turnover is important (e.g., Barbolini et al., 2020; Hutchinson et al., 2021; Pound and Salzmann, 2017; Wolfe, 1994). A number of paleobotanical and geochemical studies consistently suggest that the decrease in continental temperatures was particularly marked during winter months, thus leading to higher seasonal temperature contrasts, which are designated as a potential driving mechanism for biotic turnovers (e.g., Eldrett et al., 2009; Mosbrugger et al., 2005; Page et al., 2020; Utescher et al., 2015; Zanazzi et al., 2015). Evoked forcing mechanisms explaining this enhanced winter cooling are pCO₂, Antarctic ice-sheet (AIS) inception and increased continentality, although it remains difficult to quantify and disentangle their respective contribution from field data only. Understanding the drivers of these seasonal changes is thus important, not only for assessing the climate system behavior under major pCO₂ variations but also to better describe the paleoenvironmental context associated with major extinction events of the EOT such as the Grande Coupure in Europe and the Mongolian Remodeling in central Asia (Meng and McKenna, 1998; Stehlin, 1909; see Coxall and Pearson, 2007, for a review).

By comparing paleoclimate simulations to a synthesis of indicators of seasonality changes (Table S1 in the Supplement), our study attempts to retrieve the evolution of seasonal temperature contrast from the middle Eocene to the early Oligocene. The EOT is reconstructed step by step from five simulations, describing the evolution of three major forcings of this time: the pCO₂ drawdown, the AIS expansion and the resulting sea-level lowering, in order to understand the respective contribution of each component to the resulting seasonality change patterns, along with their possible synergies and retroactions.

## 1.2 Temperature seasonality and its evolution

Temperature seasonality can be quantified by the mean annual temperature range (MATR), which consists of the temperature difference between the warmest and coldest months of the year. Increasing MATR can occur through increased summer temperatures, lowered winter temperatures or both. MATR is practical because it can be directly calculated from temperature proxies providing an estimation of the lowest

and highest temperatures within a year. This is the case for the Climate Leaf Analysis Multivariate Program (CLAMP), which reconstructs temperatures from the modern correlation between climate variables and leaf physiognomy (Wolfe, 1993; Yang et al., 2011), or the Coexistence Approach (CA), which uses modern relatives of fossil species to define a mutual climate range of environmental characteristics (Grimm and Potts, 2016; Mosbrugger and Utescher, 1997; Utescher et al., 2014). MATR can also be deduced from the variability of the temperature signal in geochemical proxies for temperatures, stable oxygen isotopes (δ¹⁸O), as the time resolution of the proxy rarely allows for the direct reconstruction of seasonal temperatures (e.g., Ivany et al., 2000; Wade et al., 2012).

The spatial distribution of temperature seasonality changes across the EOT appears to be heterogeneous in proxy data (e.g., Pound and Salzmann, 2017). Most changes are described in the Northern Hemisphere from paleobotanical reconstructions and converge to show seasonality stronger in the early Oligocene than in the mid- to late Eocene. In North America, western and central Europe, seasonality increase is recorded by the decline in species characteristic of warm paratropical to temperate environments such as palms (e.g., *Nypa* sp.), plants from the myrtle and eucalyptus family (Myrtaceae, e.g., *Rhodomyrtophyllum* sp.), conifers (e.g., *Doliostrobus* sp.) and some plant families with tropical elements (e.g., Annonaceae, Lauraceae, Cornaceae, Icacinaceae, Menispermaceae), and, depending on the bioclimatic zones, the expansion of temperate to boreal vegetation through the increase in deciduous and/or coniferous species (Kunzmann et al., 2016; Kvaček, 2010; Kvaček et al., 2014; Mosbrugger et al., 2005; Utescher et al., 2015; Wolfe, 1992). These vegetation changes are associated with a decrease in the coldest month mean temperature (CMMT) across the EOT and start before the EOT at some localities, during the mid- to late Eocene (Moraweck et al., 2019; Mosbrugger et al., 2005; Tanrattana et al., 2020; Tosal et al., 2019; Utescher et al., 2015; Wolfe, 1994). Isotopic analyses have documented this seasonality increase in different continental localities between the Priabonian (37.8 to 33.9 Ma) and the Rupelian (33.9 to 27.82 Ma; Grimes et al., 2005; Hren et al., 2013; Zanazzi et al., 2015). While some of the changes are not directly quantifiable (e.g., the reduction in gastropod growing season length, United Kingdom; Hren et al., 2013; dental morphological changes for grazing perissodactyls, Europe; Joomun et al., 2010), others can demonstrate strong MATR increase (e.g., amplified by 15.6 °C, Canada; Zanazzi et al., 2015). A temperature seasonality increase is also documented for shallow waters of the Gulf of Mexico (increase in the MATR; Ivany et al., 2000; Wade et al., 2012). Some studies have suggested a link between increased temperature seasonality and latitude (e.g., Zanazzi et al., 2007, 2015), but data seem insufficient to validate this relationship, and this trend has not been confirmed by recent palynological compilation (Pound and Salzmann, 2017).
Data from southeast Europe and Anatolia show generally weaker and heterogeneous changes in temperature seasonality, with either no seasonality changes, slight seasonality lowering or slight seasonality strengthening from the mid-Eocene to the Rupelian (Bozukov et al., 2009; Kayseri-Özer, 2013). This variability has been explained by a strong marine influence on this part of Eocene Europe (Kayseri-Özer, 2013). Conversely, north and East Asia temperature seasonality evolution is more comparable to western Europe and North America trends (Quan et al., 2012; Utescher et al., 2015). Vegetation changes reflect an increase in the seasonal temperature range, mainly through the EOT (MATR increase of 2 to 2.5 °C; CMMT decrease of ~ 2.2 °C, Quan et al., 2012; Utescher et al., 2015). The appearance of tubers in lotus (Nelumbo sp.) during the Eocene suggests the establishment of a dormant phase in these plants and thus of a period unfavorable to plant growth (Li et al., 2014). Fossils showing these structures have been described in southern China (Hainan Province) and easternmost Russia (Kamchatka Peninsula) leading to the hypothesis that they could be favored by cooling and increased seasonality on the East Asian continent during the Eocene (Budantsev, 1997; Li et al., 2014).

In the Southern Hemisphere, studies of Paleogene localities are rarer. Despite a record of late Eocene cooling in Australia, New Zealand and Patagonia, independent proxies (stable isotopes on teeth, bones and pedogenic carbonates, palaeobotanical reconstructions) do not suggest a marked temperature seasonality during the Eocene (Colwyn and Hren, 2019; Kohn et al., 2015; Lauretano et al., 2021; Nott and Owen, 1992; Pocknall, 1989). In Australia, the presence of more pronounced tree rings suggests a late Paleogene increase in seasonality starting in the mid-Oligocene at the earliest (Bishop and Bamber, 1985; Nott and Owen, 1992). Finally, the environmental and climatic impact of the EOT in continental Africa remains poorly documented (Hutchinson et al., 2021; Saarinen et al., 2020).

## 1.3 Previous model work

Different modeling studies have illustrated the priming role of *pCO₂* lowering during the EOT, but most focused on oceans through mean annual temperature changes (Baatsen et al., 2020; Goldner et al., 2014; Hutchinson et al., 2018, 2021; Kennedy et al., 2015; Kennedy-Asser et al., 2019, 2020; Ladant et al., 2014b). The model intercomparison study of Hutchinson et al. (2021) has shown a reasonable agreement between modeling experiments with 1120 and 560 ppm (i.e., 4 and 2 times the preindustrial atmospheric levels, respectively, with 1 PAL = 280 ppm and hereafter written “4X” and “2X”) and proxy-data atmospheric and surface ocean temperature reconstructions from the late Eocene and the early Oligocene, respectively. They show, however, that changes in EOT sea surface temperatures (SSTs) were on average best represented by a *pCO₂* shift

from 910 to 560 ppm (i.e., a drop of 1.6X). In addition, a recent model-data study of Lauretano et al. (2021) explored Australia climate evolution through the EOT, and estimated a *pCO₂* drop ranging from 260 to 380 ppm (drop of ~ 0.9–1.3X). A first attempt to explain temperature seasonality change across the EOT was made by Eldrett et al. (2009). In their palynological and modeling study, Eldrett and coauthors explained high-latitude (Greenland) seasonality strengthening by *pCO₂* drop and the consequent increase in sea-ice formation over the Arctic Ocean. In their experiment, sea-ice extension induces a strong albedo feedback, which results in a large decrease in atmospheric temperature during winter. Additionally, changes in geography (topography, land-sea distribution) may have significant effects on terrestrial temperatures at a regional scale (e.g., Lunt et al., 2016; Li et al., 2018). EOT modeling experiments yield mixed answers regarding the temperature feedback resulting from both AIS and contemporary paleogeographic changes (opening of Southern Ocean gateways, Antarctic geography or global geography; Goldner et al., 2014; Hutchinson et al., 2021; Kennedy et al., 2015; Ladant et al., 2014a, b). The few studies testing the combined effect of both AIS and paleogeographic changes (Kennedy et al., 2015; Lauretano et al., 2021) suggested a moderate impact of AIS on global climate sensitivity, as previously suggested by other modeling work (Goldner et al., 2013; Kennedy et al., 2015).

# 2 Material and methods

## 2.1 Model and simulation setting

We used the IPSL-CM5A2 general circulation model, which is built upon the CMIP5 Earth system model developed at the Institut Pierre-Simon Laplace (IPSL), i.e., IPSL-CM5A-LR (Dufresne et al., 2013; Sepulchre et al., 2020). The IPSL-CM5A-LR Earth system model is composed of the LMDZ atmospheric model (Hourdin et al., 2013), the ORCHIDEE land surface and vegetation model (Krinner et al., 2005), and the NEMO v3.6 ocean model which includes modules for ocean dynamics (OPA8.2; Madec, 2016), biochemistry (PISCES; Aumont et al., 2015) and sea ice (LIM2; Fichefet and Morales-Maqueda, 1997). The atmospheric grid has a horizontal resolution of 3.75° longitude per 1.875° latitude (96 × 95 grid points) and is divided into 39 vertical levels. For a more detailed description of the model and its different components, the reader is referred to Sepulchre et al. (2020).

Five simulations were carried out to reconstruct the evolution of temperature seasonality from the middle Eocene to the early Oligocene (Table 1). The applied 40 Ma palaeogeography framework is the map developed by Poblete et al. (2021) and already used in Tardif et al. (2020) and Toumoulin et al. (2020). It features common late Eocene geography characteristics such as an open Panama Seaway, an open Tethys with a submerged Arabian Peninsula, a strongly maritime Europe, a Turgai land bridge connecting north-
ern Europe with Asia and narrow Southern Ocean gateways (Fig. 1). The orbital parameters were set to preindustrial values and the solar constant was reduced accordingly to its Eocene value (1360.19 W m⁻²; Gough, 1981). Vegetation was implemented as a boundary condition, using a zonal band of PFTs using modern vegetation distribution patterns.

Simulations were compared in pairs to highlight differences between the middle/late Eocene and the early Oligocene. The simulation set is composed of both realistic and idealized experiments (Table 1). Simulations 4X, 3X and 2X represent most of the pCO₂ range described from the mid-Eocene (Lutetian) to the early Oligocene (Rupelian; Foster et al., 2017). These pCO₂ values enable the description of the pCO₂ reduction effect on climate through this time interval and have been used in most previous modeling experiments on the EOT (Hutchinson et al., 2021). Simulations 4X and 3X cover the range of potential climate values prior to the EOT (Lutetian to Priabonian). The idealized simulation 2X allows the identification of a 1 to 2 PAL pCO₂ lowering alone. In a complementary way, simulations 2X-ICE and 2X-ICE-SL describe the early Oligocene climate, following the Antarctic ice-sheet formation. Both simulations are parameterized in the same way apart from the sea level, which is 70 m lower in 2X-ICE-SL. The use of 2X-ICE provides a theoretical description of the effect of an ice-covered Antarctica on climate, while 2X-ICE-SL constitutes a more realistic representation of the early Oligocene climate. In these experiments, the Antarctic ice cap was set to 32.5 × 10⁶ km³ according to Ladant et al. (2014b). The 70 m sea-level drop was defined following eustatic drop estimates for the EOT (Coxall et al., 2005; Katz et al., 2008; Lear et al., 2008; Miller et al., 2020). It is responsible for important geography changes related to an increase in land proportion, such as the emergence of the Arabian Peninsula and the retreat of the proto-Paratethys epicontinental sea.

All simulations run for 4000 years until temperatures indicate a quasi-equilibrium with only negligible temperature drift within the global mean ocean (< 0.1 °C per century; Fig. S1 in the Supplement). These trends are consistent with most model studies and do not affect the quality of atmospheric change described in this study (e.g., Hutchinson et al., 2018; Lunt et al., 2016). The results considered here are averages of the last 100 years of the model runs.

## 2.2 Data compilation

Simulation results were compared to MATR changes (ΔMATR) documented by proxy-data records (Table S1 in the Supplement). We compiled published MATR and CMMT proxy data from various research fields: paleobotany (macro-fossils and palynology), geochemistry (isotopic measurements on various material) and paleontology. The data were selected to range from the Lutetian (47.8 Ma) to the end of the Rupelian (27.8 Ma). This large time interval allows the representation of seasonal temperature changes parallel to

the long-term cooling of the Eocene. The inclusion of data from the middle Eocene allows a comparison with simulations testing the effect of a pCO₂ lowering alone, before AIS formation at the EOT. It is justified by the presence of paleobotanical records suggesting a strengthening of the seasons already from the Lutetian to the Priabonian (e.g., Li et al., 2014; Mosbrugger et al., 2005). Compiled Eocene–Oligocene ΔMATRs correspond either to the values given in original publications, when they were available and precise, or to values recalculated from the original data. For publications for which ΔMATRs were recalculated, we proceeded by grouping the closest sites (especially in terms of latitudes) and checked that the values obtained were consistent with the authors’ original interpretation of the paleoenvironmental context. Half of the data come from the pollen compilation of Pound and Salzmann (2017). A selection was made through this study data to keep (1) the best-dated samples, according to their dating quality indicator (data Q1 to Q3; Pound and Salzmann, 2017), and (2) sites with temperature estimates for the Priabonian and Rupelian or at least one nearby locality that could be compared. No Eocene–Oligocene site was selected for more clarity. In an effort to limit the addition of overly uncertain ΔMATR data, sites with a range of CMMT estimates (CMMTmax–CMMTmin) ≥ 10 °C (either for Priabonian or Rupelian sites) were excluded.

Some previously published seasonality increases were not associated with estimates of ΔMATR because seasonality strengthening was either (1) suggested from other parameters, such as the length of the growing season, which does not allow the calculation of the MATR (Hren et al., 2013), or (2) derived from qualitative data that cannot be specifically associated with temperature values (e.g., morphological changes such as tooth shape or plant tuber appearance; Joomun et al., 2010; Li et al., 2014). These sites are displayed on the maps in orange but are not included in quantitative analyses (Sect. 2.3). In order to better estimate the impact of changes in temperature seasonality, the length of the plant growing season (i.e., the number of months with an average temperature above 10 °C) was recalculated using the formula of Grein et al. (2013) for Coexistence Approach data (Table 1). Paleocoordinates for every locality were reconstructed using the online service of Gplates (https://www.gplates.org/, last access: 16 February 2022), according to the 40 Ma paleogeography used for the paleoclimate models (Poblete et al., 2021) that essentially follows the plate tectonic reconstruction model of Matthews et al. (2016) with some modifications.

## 2.3 Comparison of model and data ΔMATR

Different analyses were made to evaluate the data–model agreement for temperature seasonality changes from the Priabonian to Rupelian (Table 2). Modeled ΔMATR values were extracted from a 3° longitude by 3° latitude area surrounding each data locality. First, a general agreement per-
<table><caption>**Table 1.** Experimental design. Abbreviations: AIS – Antarctic ice-sheet volume (Ladant et al., 2014b); %Land – total land surface (millions of square kilometers – $10^6$ km$^2$); MAT – mean annual global 2 m air temperature (°C); SST – sea surface temperature (°C). Simulations with an asterisk constitute realistic middle Eocene (Lutetian/Bartonian) and early Oligocene (Rupelian) scenarios; others are either sensitivity experiments (2X, 2X-ICE) or use the high value of the $pCO_2$ range estimated for the time interval (4X).</caption><thead><tr><th>Simulation</th><th>pCO<sub>2</sub></th><th>AIS</th><th>%Land (Mkm<sup>2</sup>)</th><th>MAT (°C)</th><th>SST (°C)</th></tr></thead><tbody><tr><td>4X</td><td>1120 ppm</td><td>–</td><td>132.3</td><td>26.4</td><td>28.2</td></tr><tr><td>3X*</td><td>840 ppm</td><td>–</td><td>132.3</td><td>23.7</td><td>25.9</td></tr><tr><td>2X</td><td>560 ppm</td><td>–</td><td>132.3</td><td>20.6</td><td>23.2</td></tr><tr><td>2X-ICE</td><td>560 ppm</td><td>32.5 × 10<sup>6</sup> km<sup>3</sup></td><td>132.3</td><td>19.7</td><td>22.9</td></tr><tr><td>2X-ICE-SL*</td><td>560 ppm</td><td>32.5 × 10<sup>6</sup> km<sup>3</sup></td><td>152.7</td><td>18.7</td><td>22.2</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Paleotopographic 40 Ma map: (a) standard version as used for simulations 4X, 3X, 2X and 2X-ICE; (b) version adjusted with a homogeneous 70 m sea-level lowering used for the simulation 2X-ICE-SL (Poblete et al., 2021).</figcaption></figure>

centage was calculated from the direction of seasonality changes alone to assert the agreement between our simulation and qualitative data. For this metric, model predictions are considered “good” for an individual site if the modeled ΔMATR changes in the same direction as the data (i.e., if a modeled ΔMATR increases/decreases at the location of a data point showing seasonality increase/decrease). For data indicating null ΔMATR, good agreement was considered to exist when model values ranged from –0.5 to 0.5 °C.

In addition, Priabonian to Rupelian seasonality changes were compared to model predictions, by (1) testing their correlation and (2) calculating the root of the mean squared distance between their values. These two analyses were performed using R (version 4.0.3; R CoreTeam, 2020, Boston, USA). Given the limited number of quantitative Priabonian–Rupelian data ($n = 29$), the statistical correlation of data–model ΔMATR was assessed from average ΔMATR with the non-parametric Spearman rank test. In this analysis, we used the common significance level, $\alpha$, of 0.05 (i.e., p values < 0.05 indicate significant correlations). The root mean squared estimate consists of calculating the root of the mean squared distance between model and data values for comparable points (RMSE; see Kennedy-Asser et al., 2020, and their Fig. S1 in the Supplement for a detailed presentation

of the method). Conversely to the Spearman rank test, for which mean ΔMATR estimations were used, the distance is here measured using the full range of estimates at each data locality (i.e., minimum and maximum ΔMATR). Note that, because it considers the full range of ΔMATR, this method tends to minimize the difference between model and data. The lower bound of modeled ΔMATR at each locality was calculated as the difference between the lowest MATR value over the $3^\circ \times 3^\circ$ area centered around the locality for an Oligocene-like cold simulation (2X, 2X-ICE or 2X-ICE-SL) and the highest MATR value over the $3^\circ \times 3^\circ$ area for an Eocene simulation (4X or 3X). For the upper bound, we used the difference between the higher MATR value over the same area for an Oligocene-like cold simulation and the lower MATR value for an Eocene simulation.

The RMSE adjusted to ΔMATR is written as follows:

$$RMSE_{(\Delta MATR)} = \sqrt{\frac{\sum_{i=1}^{n} (\Delta MATR_{(\text{data})} - \Delta MATR_{(\text{model})})^2}{n}} \quad (1)$$

where $\Delta MATR_{(\text{data})}$ and $\Delta MATR_{(\text{model})}$ are MATR Priabonian to Rupelian changes estimated by data and model, respectively, and $n$ is the total number of localities.
# 3 Results

## 3.1 Simulated response to pCO₂ lowering

In this section, we compare the simulations 4X, 3X and 2X together to describe the effects of pCO₂ drawdown on climate and provide a range of possible MAT and MATR change intensities. The simulation pair 2X–4X represents the strongest possible changes, 3X–4X the weakest changes and 2X–3X an intermediate scenario (see Sect. 2.1). Mean annual temperatures decrease strongly in our different experiments (Table 1, Fig. 2). The halving of pCO₂ from 4X to 2X alone (i.e., without AIS formation and sea-level drop) induces a global cooling of 5.8 and 5.0 °C for the air temperature and the surface ocean, respectively (Table 1). A pCO₂ drop of 1 PAL induces a 2.7 to 3.1 °C lowering of MAT and a 2.3 to 2.7 °C cooling of the SST, for 4X to 3X and 3X to 2X changes, respectively. Along with its effect on annual temperatures and regardless of its intensity, pCO₂ decrease induces zonal ΔMATR including (1) an increase in MATR at high latitudes (especially in the north), (2) a decrease in MATR across most midlatitudes and (3) moderate changes at low latitudes, which we detail in the following Sects. 3.1.2 and 3.1.3 (for Eocene MATR values, see Fig. S2).

### 3.1.1 Areas with increased seasonality

Temperature changes are characterized by polar amplification, with a stronger winter cooling at high latitudes (Fig. 2a, b, e, f, i and j), likely due to the combined effect of albedo and sea-ice feedback. Below a given threshold (situated between 2 and 3 PAL), the pCO₂ drop enables sea-ice growth over the Arctic and, to a lesser extent, the subsistence of snow on the ground during the cold season, which increases winter surface albedo (Fig. 3). In addition, seasonal sea-ice expansion limits ocean-to-air heat transfer at the highest northern latitudes and contributes to further winter cooling of the atmosphere. This preferential lowering of winter temperatures results in a large MATR increase of 5–20% (3X–4X and 2X–3X) and up to 40% (2X–4X; Figs. 4e and S3b) over high northern latitudes. Furthermore, the areas of colder winters and high MATR widen as pCO₂ decreases: MATR increases from 60° N poleward between 4X and 3X, to 50° N poleward between 4X and 2X (Figs. 4 and S4). In contrast, Antarctica shows moderate ΔMATR (regionally up to 3 °C from 4X to 3X and 6 °C from 3X to 2X) compared to high northern latitude lands (6 °C MATR increase from 4X to 3X and 10 °C from 3X to 2X). This is because Antarctica is characterized by high MATR values in all ice-sheet-free experiments (4X, 3X and 2X), resulting in low ΔMATR changes from one experiment to another (Fig. 4a and b). This important seasonality is induced by the continent's high albedo variability, as it oscillates from snow-free to snow-covered soil within a year.

### 3.1.2 Areas with decreased seasonality

Areas with decreased seasonality are characterized by summer cooling that exceeds winter cooling, which reduces the MATR (Figs. 2 and 4). The widest zones of decreasing MATR are continental regions located within the 30–50° latitudinal band. The magnitude of simulated MATR reduction depends on the pCO₂ drop considered, with a reduction from 3X to 2X and from 4X to 2X resulting in up to 20% and 30% of regional MATR decrease, respectively (Figs. 4 and S3). At lower latitudes, in Amazonia, equatorial Africa and India, seasonality decreases but to a lesser extent (Fig. 4). A variety of atmospheric and oceanic processes are likely involved in these contrasting MATR changes, depending on the region considered.

The good correlation between MATR increase (respectively, decrease) and net precipitation decrease (respectively, increase; i.e., North America, Central Asia and northern Australia) suggests a strong involvement of the hydrological cycle in this phenomenon (Figs. 5 and 6a, c and h). The pCO₂ drop tends to slow down the hydrological cycle, which results in flatter latitudinal gradients. At high latitudes, a reduction in precipitation leads to an overall decrease, while at midlatitudes to low latitudes, increased precipitation results in a general increase (Figs. 5 and 6). In these low-latitude to midlatitude zones, precipitation strengthening is more important in summer and associated with increased evaporation, which results in larger latent heat fluxes and thus in a greater cooling during summer, consistently with decreased seasonality (Fig. 6a, c, h). In addition, summer cooling is strengthened through vegetation feedback: increase favors net primary productivity which in turn contributes to evaporation and summer warmth loss (Fig. 6a, c, h). In contrast, decreased MATR in Europe and southern South America appears poorly correlated to the above-mentioned parameters (Fig. 6b and g). For Europe, the presence of sea ice over the Arctic Ocean (Fig. 3b–e) limits heat loss via the atmosphere during winter and results in a greater summer cooling of the SST (Fig. S5), which contributes to lowering European MATR. In addition, a regional increase in low-level cloud cover during summer could also contribute to lowering MATR for both Europe and southern South America through albedo feedback (5% to 15% higher low-level cloud fraction between 40–60°; Figs. 7 and S6). For southern South America, several parameters seem consistent with the MATR reduction, but it is difficult to disentangle their contribution. By amplifying the latitudinal temperature gradients, the pCO₂ drop induces a northward migration of the westerly wind maximum (by about 2° of latitude annually but less markedly during austral winter, JAS) and of the Antarctic Circumpolar Current, which delimits the Southern Hemisphere subpolar and subtropical gyres. This northward shift limits the arrival of warm subtropical waters near the poles (Fig. S7). This greater cooling in summer SST reinforces the ocean's buffering effect on atmospheric temperatures in southern South
<figure><img src="image_5.png" /></figure>

**Figure 2.** Two-meter air temperature changes (°C). JFM: averaged over January to March; JAS: averaged over July to September. Magenta lines of (d), (h) and (l) indicate shorelines before sea-level lowering. White dotted lines in (c), (d), (g), (h), (k) and (l) are the level lines encircling the 20, 40 and 45 °C cooling zones.

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

**Figure 3.** Northern Hemisphere winter sea-ice fraction and surface albedo (%).
<figure><img src="image_7.png" /></figure>

**Figure 4.** Changes in mean annual temperature range, ΔMATR (°C). Shadings are model differences calculated with a Student *t* test over the last 100 years of comparative simulations (95% confidence); white areas indicate no significant ΔMATR. Panels (e) and (f) indicate relative ΔMATR (in %) for 2X–3X and 2X-ICE-SL–3X, respectively. Symbols correspond to ΔMATR from proxy data for different time steps: Priabonian–Lutetian (triangles); Rupelian–Lutetian (inverted triangles); Rupelian–Bartonian (circles); Rupelian–Priabonian (diamonds). Orange symbols indicate qualitative values describing a temperature seasonality increase. In the case of proxies reconstructing a range of equally probable values (e.g., the Coexistence Approach), the values shown are mean values. References are displayed in Fig. 8 and available in the data compilation provided in Table S1 in the Supplement.

America and favors milder summers, and to a lesser extent, cooler winters, which is consistent with a decrease in seasonality (Figs. 4 and 7). Finally, changes in atmospheric dynamics (decrease in the width and increase in the intensity of the Hadley cell) are also visible and could have an impact on air-ocean exchanges, but much more analysis would be needed to understand their implication, which is not the focus of this paper (Fig. S8).

## 3.2 Modeled response to Antarctic ice sheet and sea-level drop

### 3.2.1 Antarctic ice sheet only

AIS formation is responsible for a supplementary 0.9 and 0.3 °C cooling of the air temperature and the surface ocean, respectively (Table 1). Its direct effect on 2 m mean annual temperatures varies regionally and is more striking over Antarctica with up to –35 °C cooling and over the Southern Ocean and Australia (Figs. 2c, g, k and S3c and d). In contrast with Arctic sea ice which increases seasonality at the highest northern latitudes, the AIS decreases southern latitude temperature seasonality (Figs. 4c and S3c and d). Indeed, simu-
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Net precipitation (precipitation – evaporation) in JAS and JFM for the late Eocene simulation 3X (**a**, **b**). Changes associated with *pCO₂* drop from 3X to 2X (differences shown are 2X–3X) for net precipitation (**c**, **d**). Magenta dashed lines contour areas with decreased seasonality (ΔMATR ≤ −1 °C) in 2X–3X simulations (blue zones in Fig. 4b).</figcaption></figure>

lations with the AIS have a yearlong white Antarctica with
high and stable albedo, which reduces seasonal temperature
variability (Fig. 3g–l).

3.2.2 Sea-level drop

Sea-level decrease alone is responsible for a 1.0 °C cooling of global MAT (0.7 °C for surface oceans) and results in considerable regional temperature changes in areas with important land-sea distribution changes (Table 1, Figs. 4d and f and S3e and f). The marine regions that become exposed after sea-level drop show the strongest increase in MATR, as they experience both winter cooling and summer warming, due to the lower thermal inertia of land compared to ocean. This seasonality strengthening in newly exposed areas occurs independently of their latitude, therefore disrupting the otherwise zonal distribution of seasonal temperature changes generated by pCO₂ drop and AIS formation (Figs. 4f and S3f). The effect on seasonality of these disappearing seas expands beyond areas adjoining the emerging landmasses due to the resulting regional perturbation in temperature. Northern Africa, western Asia and Russia are the most impacted areas, due to the retreat of the proto-Paratethys sea and the emergence of the Arabian Peninsula (Fig. 1). More moderate seasonality changes are also visible as a result of sea retreats of smaller extent, such as the emergence of the Florida

platform and the modification of the East Asian coastlines
(Figs. 4d and S3e and f).

3.3 Model-data comparison

3.3.1 pCO₂ lowering

The ΔMATR described by the pCO₂ drop experiments (from 3X or 4X to 2X) show no good agreement either with middle to late Eocene or with late Eocene to early Oligocene data estimates (Figs. 4a and b and 8b). The simulations predict no change, or a MATR decrease, in areas where the Lutetian–Priabonian data points (n = 6, triangles, Fig. 4a) describe increased seasonality (Fig. 4a and b). Priabonian–Rupelian ΔMATR modeled through 4X to 2X and 3X to 2X pCO₂ drops is, on average, lower than data estimates at similar localities, with a mean offset of –3.5 and –2.8 °C, respectively (Fig. 8b; Table 2). The use of the simulation 4X instead of 3X for the late Eocene stage has a marginal effect on the percentage of agreement for the sign of the ΔMATR, although a slightly higher value is observed for 3X–2X (Table 2, line “%”, Figs. 8b and S9). In addition, the pCO₂ drop alone leads to zonal ΔMATRs which do not transcribe the spatial heterogeneity visible in data. This misfit is visible through high RMSE scores and the absence of significant correlation between modeled ΔMATR resulting from the pCO₂ drop (simulations 2X–4X and 2X–3X) and ΔMATR described by
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Annual variability of multiple climate parameters within the different areas of decreasing seasonality between 3X and 2X (a–c, g, h): atmospheric temperature (black), latent heat fluxes (soil to atmosphere; brown), hydrological cycle (including precipitation, evaporation and net precipitation; different shades of blue) and net primary productivity (green). Temperature changes and summer–winter temperature changes (d–f). Rectangles outline the land areas analyzed in (a)–(c), (g) and (h) (ocean zones are not taken into account in the calculation of the plots). Gray arrows show where decreasing summer temperatures match increased latent heat fluxes.</figcaption></figure>

proxy data (Table 2). Two data-model agreement patterns are nevertheless to be noted: (1) regardless of their values (which are higher in data than in our simulations), the northernmost data points are inside or surround the high-latitude seasonality strengthening zone we modeled (Fig. 4, data points 1, 2 and 3 in Fig. 8a); (2) none of the Southern Hemisphere data localities showing no seasonality change are located within MATR increase zones (Fig. 4b).

### 3.3.2 Antarctic ice sheet and sea level

The formation of the AIS alone does not result in a better agreement between modeled and Priabonian–Rupelian ΔMATR estimates. It is even slightly reduced (Table 2). The reinforcement of the MATR lowering zone at high southern latitudes increases the data-model misfit because of data points indicating null ΔMATR in this zone (Figs. 4c and 8b). There is still no significant correlation between ΔMATR from the model and differences documented by proxy data (Table 2).
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Two-meter atmospheric temperature across western Europe and Patagonia. Shadings correspond to temperatures (a, b, e, f) and temperature differences (c, d, g, h). Similarly, magenta lines contour low-level fraction (a, b, e, f) and low-level cloud fraction changes (c, d, g, h; always expressed in %) and arrows, 850 hPa winds (a, b, e, f) and 850 hPa wind changes (c, d, g, h; always in m s⁻¹). White dashed lines contour areas with decreased seasonality (ΔMATR ≤ −1 °C) in 2X–3X simulations (blue zones in Fig. 4b).</figcaption></figure>

Geographic changes associated with sea-level drop result in a better agreement with data ΔMATR (Figs. 4d and 8b). The largest continental fraction changes affect the MATR on a broad geographic scale and allow for a better agreement, even with several data points standing away from the

regions directly impacted by the sea-level drop, as for example data estimates located on the Pacific Coast (Fig. 4d). Similarly, coastline changes along the eastern part of Africa and western Asia cause an increase in seasonality in Anatolia and central/western Europe, improving the fit between model
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Data-model comparison of ΔMATR from the Priabonian to the Rupelian. (a) Map of all data ΔMATR estimates compiled in this study (symbols refer to the time period compared for the calculation of the MATR shift; see Table S1 in the Supplement for associated references). The points marked with an asterisk have an uncertain origin (marine sediments) and have not been taken into account in the statistical analyses although they are shown here. The points marked with an asterisk have an uncertain origin (marine sediments) and have not been taken into account in the statistical analyses although they are shown here. (b) Comparison of data estimates of Priabonian–Rupelian ΔMATR (black diamonds) to modeled ΔMATR at the same localities (colored circles, calculated over a 3 × 3° area) from different pairs of simulations with 3X. Error bars are minimum and maximum data estimates of ΔMATR. The dashed black line is the LOESS curve associated with data ΔMATR estimates. Bold colored lines indicate the continental latitudinal gradient of ΔMATR on land (i.e., all longitudes averaged per degree of latitude); color-shaded intervals are the standard deviation around the average. A subfigure similar to (b) but using the simulation 4X as a Priabonian stage is available in the Supplement, Fig. S9.</figcaption></figure>

and data. These changes in temperature seasonality result in a reduction of 2 to 2.5 months in the duration of the plant growing season (as reconstructed with the formula of Grein et al., 2013; Table S1 in the Supplement). Smaller changes in coastlines such as in Florida, the Kamchatka Peninsula or

along the East Asian coast increase seasonality at a regional scale and improve the data-model fit (data points 14, 22 and 24). This better fit is transcribed through the RMSE analysis results, the lowest values being obtained when the simulation 2X-ICE-SL is used to simulate the Rupelian stage (Ta-
<table><caption>**Table 2.** Priabonian–Rupelian data–model comparison. Negative mean ΔMATR values reflect a tendency of the model to underestimate ΔMATRs. The line “%” gives the percentage of sites where the direction of ΔMATR is adequately modeled (e.g., the model described a MATR reinforcement in the zone where data indicate MATR increase). Modeled ΔMATR estimates were regarded as positive when > 0.5, negative when < 0.5, or null when in the range of [−0.5; 0.5]. ρ indicates the strength of the correlation estimated with the Spearman rank test, with associated *p* values (*p*). Note that with all *p* values being > 0.05, none of the correlations are significant.</caption><thead><tr><th></th><th>2X-4X</th><th>2X-ICE-4X</th><th>2X-ICE-SL-4X</th><th>2X-3X</th><th>2X-ICE-3X</th><th>2X-ICE-SL-3X</th></tr></thead><tbody><tr><td>Average ΔMATR model-data mismatch</td><td>-3.5 °C</td><td>-3.9 °C</td><td>-1.9 °C</td><td>-2.8 °C</td><td>-3.2 °C</td><td>-1.2 °C</td></tr><tr><td>RMSE</td><td>3.1 °C</td><td>3.4 °C</td><td>2.5 °C</td><td>2.9 °C</td><td>3.2 °C</td><td>2.4 °C</td></tr><tr><td>%</td><td>19.4 %</td><td>19.4 %</td><td>41.9 %</td><td>22.6 %</td><td>16.1 %</td><td>45.2 %</td></tr><tr><td>ρ</td><td>0.21<br>(p = 0.28)</td><td>0.27<br>(p = 0.16)</td><td>0.29<br>(p = 0.12)</td><td>0.19<br>(p = 0.32)</td><td>0.25<br>(p = 0.20)</td><td>0.29<br>(p = 0.12)</td></tr></tbody></table>

ble 2). However, there is no significant correlation between model and proxy-data Priabonian–Rupelian ΔMATR, independently of the late Eocene simulation (4X or 3X) used as initial stage (for both, Spearman rank test: ρ = 0.29, *p* value = 0.12, Table 2). This persistent mismatch may be triggered by biases from the model or the data and from the methodology used to calculate these ΔMATR, which are further detailed in the Discussion. ΔMATR is still slightly underestimated by the model (Fig. 8b; Table 2). The pair of simulations that best describe the Priabonian–Rupelian transition, according to currently available data is 2X-ICE-SL-3X as it presents (1) the lowest average model–data ΔMATR mismatch (−1.20 °C) and (2) the best agreement in ΔMATR direction (45 % of the data, Table 2).

# 4 Discussion

## 4.1 Implication for mechanisms of late Eocene to early Oligocene seasonality changes

### 4.1.1 Model climate sensitivity and climate response to EOT forcing

ΔMATR across the EOT are better predicted when considering the changes occurring between the lower *pCO₂* simulation 3X for the late Eocene stage and the most realistic simulation 2X-ICE-SL for the early Oligocene stage. In addition, with a mean SST cooling of 2.7 °C between 3X and 2X simulations (Table 1), surface temperature changes are also in agreement with the mean changes described in marine proxies across the EOT (i.e., difference of 2.9 °C between 38–34.2 and 33.7–30 Ma, Hutchinson et al., 2021). This best fit with a limited drop in *pCO₂* reflects the high climate sensitivity of our model (i.e., the average temperature change per doubling of the *pCO₂* at model equilibrium; PALAEOSENS, 2012). This high sensitivity is also highlighted in our experiments of *pCO₂* halving from 1120 to 560 ppm (2X–

4X), which result in a dramatic mean annual global cooling (5.8 °C for global MAT, 5 °C for SST, Table 1). Such a temperature difference is high compared to previous modeling studies which describe a 3 to 4 °C surface atmospheric temperature difference under a similar *pCO₂* decrease and the 2.9 °C cooling found in marine proxies across the EOT (Hutchinson et al., 2021). The *pCO₂* of 4X and 2X is commonly used in simulations to represent the transition to an icehouse world (e.g., Baatsen et al., 2020; Goldner et al., 2014; Kennedy-Asser et al., 2019). Although 4X is likely to represent the upper end of *pCO₂* possible values for the late Eocene (the average values are rather close to 800 ppm from the Lutetian to the Priabonian; Foster et al., 2017), the use of this value is justified to better reconstruct high-latitude temperatures (Huber and Caballero, 2011). A good agreement between warm conditions and Bartonian SST data has also been recently shown by other experiments using the model IPSL-CM5A2 with middle/late Eocene boundary conditions (Tardif et al., 2020; Toumoulin et al., 2020). We thus argue that although the use of the 4X simulation is appropriate to study possible variations in *pCO₂* during the Eocene, the use of the 3X simulation is better to study the changes between the Priabonian and the Rupelian.

### 4.1.2 Temperature seasonality changes through the late Eocene

The evolution of the different climate features likely involved in ΔMATR is consistent with several findings from previous studies. First, earlier modeling experiments have described albedo and sea-ice increase resulting in polar amplification of the cooling (e.g., Baatsen et al., 2020; Kennedy-Asser et al., 2019) and a reinforcement of temperature seasonality (Ellett et al., 2009). The resulting strengthening and expansion of the northern high-latitude MATR increase zone with *pCO₂* lowering is a good explanation for the dramatic seasonality increase at high latitudes suggested by some studies
(Eldrett et al., 2009; Wolfe, 1992; Zanazzi et al., 2015). In addition, changes in the magnitude and distribution of net precipitation (i.e., precipitation – evaporation) resulting from the decrease in pCO₂ agree with previous theoretical and modeling work suggesting an intensified hydrological cycle under higher pCO₂ (e.g., Carmichael et al., 2016; Hutchinson et al., 2018). This phenomenon results from a greater capacity of the air to retain moisture and more intense atmospheric convection phenomena (Allen and Ingram, 2003; Carmichael et al., 2016; Held and Soden, 2006). In parallel, although the implication of changes in the atmospheric circulation in the southern South American seasonality lowering zone appears non-obvious, the intensification and weakening of the Hadley cell extent in relation to changing pCO₂ levels have been described numerous times (e.g., Lu et al., 2007; Frierson et al., 2007). Deeper analyses would be needed to understand the atmospheric dynamics in the simulations, which is beyond the scope of the study. Finally, the increase in low cloud cover is consistent with previous model studies describing a higher fraction of low-level clouds under lower pCO₂ (Baatsen et al., 2020; Caballero and Huber, 2013; Zhu et al., 2019). Nevertheless, although a low-level cloud cover increase due to the pCO₂ drop is consistent with increased air moisture in western Europe at the EOT (Kocsis et al., 2014), this parameter remains poorly constrained in paleoclimate archives and modeling analysis (Lunt et al., 2020; Sagoo et al., 2013).

Despite these agreements, the MATR evolution resulting from the pCO₂ drop does not clearly match data estimates, whether they correspond to both Lutetian–Priabonian or to Priabonian–Rupelian changes. This suggests that the temperature seasonality inferred from proxy data can only be partly explained by a pCO₂ drop. Since zonal ΔMATR patterns are simulated with a pCO₂ drop of 1 PAL (either from 4X to 3X or from 3X to 2X), we hypothesize that they likely occurred before the AIS onset and that the strengthening of seasonality occurred in northern high latitudes in the first place. However, most Lutetian–Priabonian data are not located in the high latitudes, which prevents unambiguous testing of this hypothesis (Fig. 4a and b and Table S1 in the Supplement). Similarly, the presence of areas with decreased seasonality due to changes in the hydrological cycle (i.e., the USA, Central Asia, north Australia) cannot be confirmed because of a lack of data in these areas: although some of the data associated with a decrease in the MATR share the same latitudinal bands, none of them are directly located within a zone of MATR decrease. New additional seasonal temperature records in these areas would be interesting to better trace such eventual early trends. The general low fit of data and model values for middle to late Eocene changes is, to some extent, predictable since the ice-free 2X simulation does not represent the late Eocene (see “Material and methods” section) and was designed as a sensitivity test. Indeed, small-scale glaciations (25%–35% modern AIS) may have existed during the late Eocene, before the EOT, associated with a moderate sea-level decrease (Carter et al., 2017; Miller et al.,

2020; Scher et al., 2014). Interestingly, the combination of the three forcing mechanisms leads to a better agreement of the modeled ΔMATR with some of the few middle to late Eocene data, especially in coastal areas of Kamchatka, and southern China (triangles, Fig. 4). Although the 70 m sea-level decrease prescribed in the 2X-ICE-SL simulation is unrealistic for the late Eocene, the better data–model agreement when both AIS and sea-level decrease are considered suggests that small ice-sheet development before the EOT may have played a significant role in driving the middle to late Eocene ΔMATR. Additional sensitivity experiments, with a smaller AIS and an intermediated sea-level drop, may allow further quantification of the sensitivity of coastal localities to sea-level variations occurring before the EOT.

### 4.1.3 Temperature seasonality changes through the EOT

The use of two simulations to set up the effect of the AIS onset (with or without a drop in sea level) is interesting to unravel the direct and indirect mechanisms affecting temperatures. Temperature changes resulting from the presence of the AIS alone (i.e., not taking into account sea level) are consistent with previous model studies that simulate its highly regional effect on atmospheric temperature (see the Supplement of Hutchinson et al., 2021), although the changes in our simulations spread more widely over the Southern Ocean and Australia. The decreasing seasonality zones modeled at high latitudes and midlatitudes of the Southern Hemisphere are mostly associated with an absence of seasonality change in the data, which often display stable vegetation and biomes from the late Eocene to the Rupelian (Hutchinson et al., 2021; Kohn et al., 2015; Nott and Owen, 1992; Pocknall, 1989; Pound and Salzmann, 2017). This apparent mismatch calls into question the capability of paleobotanical proxies to record a temperature seasonality decrease in environments already characterized by low seasonality. Indeed, the decrease in the temperature seasonality is associated with a more pronounced drop in summer temperatures, which is a less limiting factor for flora distribution and thus less constrained in the fossil record than winter temperatures (Huber and Caballero, 2011).

The evolution of temperature seasonality from the Priabonian to the Rupelian is better represented when the sea-level drop associated with the AIS is taken into account (Table 2, Figs. 4 and 8). This consequence of the Antarctic glaciation has global repercussions and explains part of the heterogeneity documented in the data, as previously suggested (Pound and Salzmann, 2017). Our results are very dependent on the paleogeography used in the simulations and of the proxy location used in our data–model comparison. Because our Rupelian simulations use a late Eocene paleogeography with a global sea-level decrease, we overlook some paleogeographic changes that occurred between both periods, which may affect our seasonality reconstruction. The
gradual northward migration of Australia is not considered; the Neotethys is gradually closed during the early Oligocene, but a deep-sea passage to the north of the Arabian Plate remains present in our paleogeography (Barrier et al., 2018). Another source of error may come from fragmented continental areas such as those seen in Europe at that time. In these zones, temperature changes recorded through the EOT are heterogeneous as paleovegetation studies suggest medium (1.8–2.1 °C; Moraweck et al., 2019; Teodoridis and Kvaček, 2015; Tosal et al., 2019) to strong (up to 8.3 °C; Tanrattana et al., 2020) MATR increase. The heterogeneity shown in data might thus result from smaller-scale paleogeographic changes through the EOT that are not well represented by the resolution used in our simulations. This variability of the data ΔMATR estimates could also be due to (1) a variable quality of MATR data related to the fragmentary nature of the fossil record and to differential recording of vegetation types as well as (2) differences in the temperature of marine/oceanic zones before regression. Depending on their extension and depth, these seas may have buffered seasonal temperature variations in the nearby regions more or less importantly, and therefore their disappearance may have affected the MATR at different magnitudes. An early Oligocene intensification of seasonality in central and eastern Europe, associated with a major phase of Antarctic ice-sheet expansion (and its effect on sea level), is consistent with fairly stable vegetation between the middle and late Eocene (Bozukov et al., 2009; Kvaček et al., 2014; Moraweck et al., 2015). This may result from the proximity with the warm Tethys, buffering the EOT cooling, as suggested by stable δ¹⁸O describing moderate temperature changes in this area (Kocsis et al., 2014).

Finally, differences between our modeling results and data may also be related to the amplitude of the sea-level drop used in our simulation compared to its variability during the Rupelian. The EOT is generally described in two steps: a first event at ~33.9 Ma with both a decrease in temperature and sea level (~25 m) and a second event, the Early Oligocene Glacial Maximum (EOGM), between approximately 33.65 and 33.15 Ma, starting after a large oxygen isotope incursion (often referred to as “Oi-1”), which is characterized by an additional 50 m sea-level decrease (see Hutchinson et al., 2021, for synthesis and terminology, and Miller et al., 2020). The sum of these two steps corresponds to the boundary conditions of our simulation. However, important variations in the East Antarctic Ice Sheet have been described until the early Miocene (50–60 m sea-level equivalent; Miller et al., 2020). Directly after the EOGM phase, a decrease in ice volume is visible between 33.15 and 32.8 Ma, before it increases again and remains stable between 32.8 and 29 Ma (after the Oi-1a event; Galeotti et al., 2016). Due to the combined effects of the drop in CO₂ and the development of the AIS (and the amplitude of the associated drop in sea level, 70 m), the important changes in seasonality reconstructed here (2X-ICE-SL–3X) were probably not in place throughout the Rupelian but rather for shorter periods during the EOGM, or later be-

tween 32.8–29 Ma. Most continental paleoclimate studies do not provide the resolution to distinguish these steps. Among the data points compiled for this study, only four sites have enough temporal resolution to be linked to the EOGM phase represented by our 2X-ICE-SL simulation (Bozukov et al., 2009; Hren et al., 2013; Kohn et al., 2015; Tosal et al., 2019).

## 4.2 Perspectives on environmental and biotic crisis

The EOT is associated with major extinction events, of which the best known are the Grande Coupure in Europe and the Mongolian Remodeling in central Asia (Stehlin, 1909; Meng and McKenna, 1998; see Coxall and Pearson, 2007, for a review). Both events have been recognized as major biotic turnovers for ungulates (Blondel, 2001; Stehlin, 1909). In addition, other vertebrates were affected by the Grande Coupure (e.g., rodents, primates, amphibians and squamates), and major changes in vegetation are described, in association with the Mongolian Remodeling and regionally, in Europe (e.g., Barbolini et al., 2020; Marigó et al., 2014; Pound and Salzmann, 2017; Rage, 1986, 2013; Roček and Rage, 2003). These changes have been linked to (1) competitive interactions resulting from the dispersal of Asian taxa to Europe and (2) EOT climate deterioration and selection processes through resource and/or habitat changes (e.g., Hooker et al., 2004; Kratz and Geisler, 2010; Marigó et al., 2014; Sun et al., 2015; Zhang et al., 2012). The latter mechanism is commonly related to irreversible cooling and/or aridification at the EOT (e.g., Blondel, 2001; Sun et al., 2015; Zhang et al., 2012). Climate cooling may have significantly reduced the habitat of well-spread early Eocene tropical (and paratropical) species, which are characterized by narrow thermal ecological niches (Hren et al., 2009; Huang et al., 2020; Jaramillo et al., 2006; Wing, 1987). Although the distribution of fauna and flora is based on a complex set of parameters, we discuss here how ΔMATR provides an additional interpretative key for understanding biotic turnover at the EOT.

While North America and Asia show comparable temperature changes, our simulations highlight significant differences in the evolution of their MATR, which decreases and either increases/decreases at a regional scale, respectively. Vegetation changes and the Mongolian Remodeling are contemporaneous to AIS growth between 32.8–29 Ma and can be compared with our 2X-ICE-SL simulation (Galeotti et al., 2016; Kraatz and Geisler, 2010; Sun et al., 2015). The MATR strengthening modeled in central Asia shows that cooling was particularly strong during winter. In addition to the aridification, this more pronounced winter cooling may have contributed to the intensity of extinctions in this area (Barbolini et al., 2020; Dupont-Nivet et al., 2007). This seasonality strengthening is strongly driven by the proto-Parathethys retreat, which contrasts with a previous geochemistry study suggesting a weak contribution of this sea to local climate conditions (Bougeois et al., 2018). Conversely, the
zone of decreased MATR reconstructed in North America
may provide an explanation for the low impact of the EOT on
fauna and vegetation in this area (Coxall and Pearson, 2007;
Prothero and Heaton, 1996; Stucky, 1992). Despite a similar
decrease in the mean annual temperature, most of the temper-
ature drop is in summer and is not associated with the onset
of cold winters (Fig. S4). We hypothesize that these patterns
enabled a greater persistence of existing warm-temperate to
paratropical vegetation and associated biota (Pound and Salz-
mann, 2017). The study of Tardif et al. (2021) using the
same model and similar simulations (4X and 2X-ICE) with a
dynamic vegetation module shows moderate biome changes
across the EOT in this North America decreased seasonal-
ity zone. Reduced stress on biodiversity in areas with limited
or reduced MATR changes is also consistent with moderate
vegetation changes across the EOT in areas with decreasing
seasonality in the Southern Hemisphere (see discussion in
Sect. 4.1.3).

Europe stands in an intermediate position between North
America and Asia with generally weaker changes in MATR
(Fig. 4d); eastern Europe exhibits a slight increase in MATR,
while MATR decreases in the west. Although comparable
ΔMATR values could have different impacts depending on
initial MATR, the types of ecosystems and their resilience,
the strong consequences of moderate seasonality increase
on growing season length support the hypothesis that sea-
sonality changes may also have contributed to shaping the
biodiversity evolution in central Europe. The late develop-
ment of increased seasonality zones in this area, linked to the
major phase of sea-level drop, could explain the persistence
of fairly stable vegetation during the Eocene (Kvaček et al.,
2014; Hutchinson et al., 2021). Yet, given the fragmented na-
ture of Europe at this time, increases in seasonality prior to
the EOT would also be possible as a result of smaller, but
locally significant, sea-level variations. These differences in
the evolution of the MATR between North America, Europe
and Asia are consistent with several studies suggesting differ-
ent causes for EOT extinctions (Blondel, 2001; Hooker et al.,
2004; Meng and McKenna 1998; Sun et al., 2015). Finally,
little is known about the EOT in Africa (notably because of
a few Oligocene sites), but the data available suggest mod-
erate changes in northern African flora and fauna (Hutchin-
son et al., 2021; Jacobs et al., 2010; Pound and Salzmann,
2017; Rasmussen et al., 1992). The significant increase in
seasonality in North Africa seen in our simulations (which
results from the emergence of part of the continent) differs
from the one observed in other areas, since it is linked to an
increase in summer temperatures (barely no change in win-
ter). Unchanged winter temperatures and the shift of marine
to terrestrial environments might complicate the recording of
this temperature seasonality strengthening. Recording low-
latitude changes in seasonality strengthening may however
be possible as shown by a recent study combining multiple
lines of evidences (including plant and primate macrofos-
sils), which suggest a potential shift from tropical to more

open deciduous vegetation through the EOT, reflecting in-
creased seasonality in precipitation and/or temperature sea-
sonality during the early Oligocene at low latitudes of South
America (Peru, ~7°S, Antoine et al., 2021). More studies
would probably enable a better understanding of the evolu-
tion of the seasonality in this low-latitude area.

5 Conclusions

This study investigates the changes in temperature season-
ality during the middle to late Eocene and across the EOT.
MATR changes modeled with the combined effects of pCO2
drop, AIS formation and sea-level lowering are qualitatively
consistent with the proxy-data reconstruction of the late
Eocene to early Oligocene. The decrease in pCO2 leads to
a marked strengthening of seasonality in the northern high
latitudes, which may have started earlier than the EOT, dur-
ing the late Eocene. The formation of the AIS and the result-
ing sea-level drop lead to both an intensification and an ex-
tension of increasing seasonality areas. The best agreement
between data and modeled MATR evolution throughout the
EOT is reached when all three parameters are combined. Ac-
counting for sea-level changes associated with the Antarctic
glaciation appears to be the most important parameter to ex-
plain the heterogeneity of ΔMATR across the EOT. A sea-
sonality increase is also visible in middle to late Eocene lo-
calities, which may reflect earlier sea-level changes associ-
ated with the incipient precursors of the Antarctic ice sheet.
A discrepancy between data and model is present for quan-
titative MATR change estimates across the EOT, with less
marked seasonality changes in the model. This is mainly due
to areas where a decrease in seasonality is predicted by the
model while the vegetation proxies show stable vegetation.
We hypothesize that this discrepancy can be explained by
a low capacity of vegetation to record decreases in summer
temperatures compared to winter temperatures.

Reconstructing changes in MATR brings additional con-
straints on the abiotic environmental factors at play on land
between the middle Eocene and the early Oligocene. The
different mechanisms described here likely explain the het-
erogeneity in seasonality changes found in data across the
greenhouse-icehouse transition and provide insights into
the diversity of continental paleoenvironments. The map of
ΔMATR reconstructed here give new elements to help under-
stand major extinction events of the EOT. This study primar-
ily focused on the evolution of temperature seasonality that
has the clearest evolution. The variability of other seasonal-
ity parameters, including rainfall seasonality, will be worth
investigating in future studies to obtain a finer picture of
the evolution of terrestrial climates and biodiversity through
the EOT. In addition, further work using higher-resolution
Rupelian paleogeography and regional models would be of
great interest to better reconstruct temperature seasonality
changes, especially in highly fragmented land areas such as Europe during the Eocene.

**Code availability.** LMDZ, XIOS, NEMO and ORCHIDEE are released under the terms of the CeCILL license. OASISMCT is released under the terms of the Lesser GNU General Public License (LGPL). The IPSL-CM5A2 code is publicly available through svn, with the following command lines: svn co http://forge.ipsl.jussieu.fr/igcmg/svn/modipsl/branches/publications/IPSLCM5A2.1_11192019 (IPSL Climate Modelling Centre, 2022a), modipsl cd modipsl/util;./model IPSLCM5A2.1

The mod.def file provides information regarding the different revisions used, namely

- NEMOGCMbranchnemo_v3_6_STABLErevision666-XIOS2branchs/xios-2.5revision1763

- IOIPSL/srcsvntags/v2_2_2

- LMDZ5branches/IPSLCM5A2.1rev3591

- branches/publications/ORCHIDEE_IPSLCM5A2.1.r5307rev6336-OASIS3-MCT2.0ahan(rev4775IPSLserver).

The login/password combination requested for the first use to download the ORCHIDEE component is anonymous/anonymous. We recommend referring to the project website http://forge.ipsl.jussieu.fr/igcmg_doc/wiki/Doc/Config/IPSLCM5A2 (IPSL Climate Modelling Centre, 2022b) for a proper installation and compilation of the environment.

**Data availability.** The key climatological outputs of the simulations are stored in the PANGAEA database: https://doi.pangaea.de/10.1594/PANGAEA.930422 (Toumoulin et al., 2021).

**Supplement.** The supplement related to this article is available online at: https://doi.org/10.5194/cp-18-341-2022-supplement.

**Author contributions.** YD, AT and JBL conducted the modeling experiments. AT compiled proxy data, analyzed the model results, generated figures and tables, and drafted the paper. AT, YD, DT and JBL discussed the paleoclimate results. LK contributed to the paleobotanical context and AL and GDN to the paleogeographic and geological context. All authors have provided critical feedback and contributed to the final paper.

**Competing interests.** The contact author has declared that neither they nor their co-authors have any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Acknowledgements.** We thank the CEA/CCRT for providing access to the HPC resources of TGCC under the allocation

2018-A0030102212, and 2019-A0050102212 made by GENCI. Agathe Toumoulin and Guillaume Dupont-Nivet acknowledge the support of the ERC MAGIC under grant 649081. Yannick Donnadieu and Delphine Tardif acknowledge support from ANR AMOR (grant no. ANR-16-CE31-0020). The authors acknowledge Cheng-Sen Li for sharing a translation of the reference Bantsev (1997). We acknowledge the use of Ferret (https://ferret.pmel.noaa.gov/Ferret/, last access: 10 November 2021) and RStudio software (https://www.rstudio.com/, last access: 10 November 2021) for analysis and figures in this paper. We sincerely thank Alberto Reyes for his editorial handling and two anonymous reviewers for their interesting comments that improved the quality of this article.

**Financial support.** This research has been supported by ERC MAGIC (grant no. 649081) and ANR AMOR (grant no. ANR-16-CE31-0020). The article processing charges for this open-access publication were covered by ANR AMOR (ANR-16-CE31-0020).

**Review statement.** This paper was edited by Alberto Reyes and reviewed by two anonymous referees.

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