# Simulation of Arctic sea ice within the DeepMIP Eocene ensemble: Thresholds, seasonality and factors controlling sea ice development

Igor Niezgodzki, Gregor Knorr, Gerrit Lohmann, Daniel Lunt, Christopher Poulsen, Sebastian Steinig, Jiang Zhu, Agatha de Boer, Wing-Le Chan, Yannick Donnadieu, et al.

► To cite this version:

Igor Niezgodzki, Gregor Knorr, Gerrit Lohmann, Daniel Lunt, Christopher Poulsen, et al. Simulation of Arctic sea ice within the DeepMIP Eocene ensemble: Thresholds, seasonality and factors controlling sea ice development. Global and Planetary Change, 2022, 214, pp.103848. 10.1016/j.gloplacha.2022.103848. hal-03681710

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# Simulation of Arctic sea ice within the DeepMIP Eocene ensemble: thresholds, seasonality and factors controlling sea ice development

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Igor Niezgodzki¹,², Gregor Knorr², Gerrit Lohmann², Daniel J. Lunt³, Christopher J. Poulsen⁴, Sebastian Steinig³, Jiang Zhu⁵, Agatha de Boer⁶, Wing-Le Chan⁷, Yannick Donnadieu⁸, David K. Hutchinson⁹, Jean-Baptiste Ladant⁴, and Polina Morozova¹⁰

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¹ING PAN - Institute of Geological Sciences Polish Academy of Sciences, Research Center in Kraków, Biogeo-system Modelling Group, Senacka 1, 31-002 Kraków, Poland

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²Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

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³School of Geographical Sciences, University of Bristol, Bristol, UK

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⁴Department of Earth and Environmental Science, University of Michigan, 48109 Ann Arbor, USA

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⁵Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA

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⁶Department of Geological Sciences, Stockholm University, Stockholm, Sweden

⁷Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo, Japan

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⁸Aix Marseille Univ, CNRS, IRD, INRA, Coll France, CEREGE, Aix-en-Provence, France

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⁹Climate Change Research Centre, University of New South Wales, Sydney, Australia

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¹⁰Institute of Geography, Russian Academy of Sciences, Moscow, Russia

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**Abstract**

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**Keywords**

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early Eocene, Arctic Ocean, sea ice, Earth system model, model intercomparison
# 1. Introduction

According to IPCC Shared Socio-economic Pathway (SSP) SSP5-8.5, by the end of this century atmospheric CO₂ concentration will reach 1135 ppmv (IPCC, 2021). The newest proxy-based reconstructions based on boron isotopes from planktonic foraminifera set a lower bound (95% confidence interval) on the early Eocene Climatic Optimum (EECO, ~49-53 Ma) atmospheric CO₂ at ~1170 ppmv (Anagnostou et al., 2020). Therefore, early Eocene greenhouse conditions supported by the high atmospheric CO₂ concentrations are considered as one of the best analogues for future high CO₂-driven climate (Burke et al., 2018). As part of the international DeepMIP collaboration (Lunt et al., 2017, 2021), modelling groups conducted simulations of early Eocene climate with the same paleogeography and atmospheric greenhouse gas concentrations to explore inter-model variability and enable systematic investigation of early Eocene climate features.

One of the most important questions regarding near future climate is how sea ice might respond in the Arctic Ocean under elevated atmospheric CO₂ concentrations. In the last decades, Arctic sea ice has been shrinking drastically (IPCC, 2021; Simmonds and Li, 2021). The CO₂ threshold when permanent sea ice will no longer be observed in the Arctic Ocean is unknown. Based on simulations of late Miocene climate, Stein et al. (2016) indicated that at CO₂ level of 450 ppmv the Arctic Ocean was ice-free during the summer months. On the other hand, Poulsen and Zhou (2013) and Niezgodzki et al. (2019) simulated seasonal sea ice formation in the Arctic Ocean during the greenhouse of the middle and late Cretaceous. Poulsen and Zhou (2013) report seasonal ice in the Arctic Ocean at CO₂ levels up to 10x PI, while Niezgodzki et al. (2019) set the threshold for seasonal sea ice formation, independent of gateway configurations between 3x and 4x PI CO₂ levels. Therefore, it is important to investigate sea ice presence in the Arctic Ocean also during different greenhouse climates in the Earth's history with high atmospheric CO₂ levels.

Unfortunately, proxy data from the Arctic Ocean for deep-time climates are sparse. Our knowledge of the Arctic sea ice in deep-time greenhouse climates is limited and can be controversial (see Jenkyns et al. (2004) vs. Davies et al. (2009) for the Cretaceous). Additionally, some proxy-based (e.g. Tex86) temperature reconstructions are controversial and can lead to overestimates of Arctic Ocean temperatures (Ho & Laepple, 2016; Jenkyns et al., 2004). Furthermore, CO₂ levels are less strictly constrained than for more recent climates. Nevertheless, recent proxy-based reconstructions suggest that seasonal sea ice was present in the Arctic Ocean as early as ~47.5 Ma while perennial (although ephemeral) sea ice was first reported for ~44 Ma (Darby,
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2014; Stickley et al., 2009). However, Stein et al. (2015) dispute the presence of perennial sea ice and argue that only seasonal sea ice formed in the middle Eocene. Tripati and Darby (2018) find ice-rafted grains in the Greenland Sea originating from Greenland ~47 Ma indicating formation of glacial ice in this region. Other studies also show glacial ice around the Arctic Ocean dated from the middle Eocene (St. John, 2008; Tripati et al., 2008). Taken together, past studies suggest that at least transient land and sea ice was present in the Arctic realm even during a high CO₂ greenhouse world. On the other hand, Eldrett et al. (2009) show that the cold month mean temperatures (CMMT) on Greenland were > 5° C in the middle Eocene. These reconstructions preclude formation of even seasonal sea ice. Furthermore, there is no geological evidence for the presence of Arctic sea ice during the EECO. CMMT over land during early Eocene was above freezing apart from the interior of Antarctica and high altitude regions (Huber and Caballero, 2011).

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DeepMIP provides a unique opportunity to investigate the early Eocene climate using a multi-model approach. Although all participating models should have been run with identical paleogeographic boundary conditions (Fig. 1), minor local adjustments (such as width/depth of the gateways or opening/closure of narrow seaways) were sometimes necessary to avoid instabilities or due to other model requirements. Even small gateway modifications can have important effects on regional climate by changing water mass exchange between the basins (e.g., Hunter et al., 2013; Knies et al., 2014; Roberts et al., 2009; Stärz et al., 2017; Vahlenkamp et al., 2018; Hutchinson et al., 2019). Furthermore, the models differ in ways that can affect the development of sea ice. One such difference is their application of river discharge models which can influence seawater salinity especially in a highly enclosed basin like the Arctic Ocean. Niezgodzki et al. (2019) show that salinity can have a decisive role in the control of sea ice formation in the Arctic Ocean by changing the freezing point temperature. Therefore, it is important to investigate the physics reasons that lead to the differences between the sea ice modelling results across the ensemble.

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Here we compare simulated Arctic sea ice across the ensemble of models participating in Eocene DeepMIP. We identify the threshold for both seasonal and perennial sea ice in the Arctic Ocean (sections 3.1 and 3.2 respectively) and discuss the physical mechanisms (section 3.3) that lead to differences in thresholds across the model ensemble. Finally, we assess our results in a geological context (section 4) by comparing them with reconstructions from proxy data.

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<figure><img src="image_2.png" /><figcaption>**Figure 1.** a) Eocene land-sea mask of Herold et al. (2014) and b) bathymetry (m) around Arctic gateways used as an input boundary condition of each group.</figcaption></figure>

## 2. Methods

The models and simulations analysed here have been previously described in the overview paper of Lunt et al. (2017, 2021). Here, we provide some details for each model that are relevant to understanding Arctic sea ice processes (see Tab. 1). Our study focuses on six of the eight DeepMIP models (CESM1.2_CAM5, COSMOS-landavg_r2413, GFDL_CM2.1, HadCM3b_M2.1aN, IPSLCM5A2 and MIROC4m). We exclude NorESM1_F from the analyses because the required monthly mean model outputs were not available. Due to different resolutions of the native grids of each model, we interpolate all results to a 360x180 grid using nearest neighbour interpolation.

### 2.1. Energy flux analyses

In our analyses we apply the modified one-dimensional energy balance model used by Heinemann et al. (2009) and Lunt et al. (2012; 2021). This method allows us to separate warming due to atmospheric CO₂ changes into different components (surface albedo, non-surface albedo, heat transport convergence, long wave clouds and greenhouse effects). However, for applications of the model to seasonal timescales we must take additional constrains into account. Equation (3) of Lunt et al. (2021) assumes that the radiation budget is closed. This is not the case for seasonal timescales due to seasonal heat storage and atmosphere-ocean heat exchange (Lee et al., 2017). Therefore, the equation (3) of Lunt et al. (2021) will have a different form because the heat transport convergence (equation (7) in Lunt et al. (2012)) balances net top-of-the-
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atmosphere short and long wave radiation imbalances only in equilibrium. As a consequence we do not differentiate between atmospheric and ocean heat flux changes.

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In the following, we investigate the effect of different heat fluxes which influence DJF warming due to the CO₂ increase from 1x to 3x PI CO₂ level in each model. Instead of calculating emissivity induced warming as given in equation (2) in Lunt et al. (2021), we split it into the greenhouse and long wave cloud effects as described by formulas (13) and (15) in Lunt et al. (2012) respectively. This allows us to separate effects of long wave clouds and water vapour contributions that are included (together with CO₂ and lapse rate) in the greenhouse effect component. It is especially important in the high latitudes where strong atmospheric moisture increase as a result of CO₂ level rise leads to surface temperature warming due to downwelling longwave radiation as shown based on the recent data for both Arctic Ocean region (Lee et al., 2017) and Antarctica (Sato and Simmonds, 2021). In our analyses we do not include any contribution of albedo effect because of the polar night in the Arctic region during DJF.
<table><caption>**Table 1.** Details of ocean components of each model included into the analyses with respective CO₂ levels sensitivity tests for each model. Additionally, threshold when sea ice disappears seasonally/permanently from the Arctic Ocean for each model is presented.</caption><thead><tr><th>Model</th><th>Short model name</th><th>Ocean Resolution</th><th>Sea ice model parametrisation</th><th>River routing</th><th>1x CO<sub>2</sub></th><th>3x CO<sub>2</sub></th><th>4x CO<sub>2</sub></th><th>6x CO<sub>2</sub></th><th>Seasonal sea ice threshold</th><th>Permanent sea ice threshold</th></tr></thead><tbody><tr><td>CESM1.2 CAM5</td><td>CESM</td><td>1.0° x 1.0°</td><td>Hunke et al. (2017)</td><td>Herold et al. (2014)</td><td>x</td><td>x</td><td></td><td>x</td><td>&gt; 1x &amp; &lt; 3x CO<sub>2</sub></td><td>&lt; 3x CO<sub>2</sub></td></tr><tr><td>COSMOS-landavg_r2413</td><td>COSMOS</td><td>3.0° x 1.8°</td><td>Hibler (1979)</td><td>Hagemann and Dümenil (1998)</td><td>x</td><td>x</td><td>x</td><td></td><td>&lt; 3x CO<sub>2</sub></td><td>&gt; 4x CO<sub>2</sub></td></tr><tr><td>GFDL_C M2.1</td><td>GFDL</td><td>1.0° x 1.5°</td><td>Hunke and Dukowicz (1997), Winton (2000)</td><td>Herold et al. (2014)</td><td>x</td><td>x</td><td>x</td><td>x</td><td>&lt; 3x CO<sub>2</sub></td><td>&gt; 3x &amp; &lt; 4x CO<sub>2</sub></td></tr><tr><td>HadCM3b_M2.1aN</td><td>Hadley</td><td>1.25° x 1.25°</td><td>Hibler (1979), Gordon et al., (2000)</td><td>Herold et al. (2014)</td><td>x</td><td>x</td><td></td><td></td><td>&gt; 1x &amp; &lt; 2x CO<sub>2</sub></td><td>&gt; 2x &amp; &lt; 3x CO<sub>2</sub></td></tr><tr><td>INM-CM4-8</td><td>INMCM</td><td>1.0° x 0.5°</td><td>Yakovlev (2009)</td><td>Herold et al. (2014)</td><td></td><td></td><td></td><td>x</td><td>&lt; 6x CO<sub>2</sub></td><td>&gt; 6x CO<sub>2</sub></td></tr><tr><td>IPSLCM5 A2</td><td>IPSL</td><td>2.0° x 2.0°</td><td>Fichefet and Maqueda (1997), Timmerman et al. (2005)</td><td>Herold et al. (2014)</td><td>x (1.5x)</td><td>x</td><td></td><td></td><td>&lt; 3x CO<sub>2</sub></td><td>&gt; 3x CO<sub>2</sub></td></tr><tr><td>MIROC4m</td><td>MIROC</td><td>1.4° x 0.92°</td><td>Semtner (1976), Hibler (1979), Hunke and Dukowicz (1997)</td><td>Herold et al. (2014)</td><td>x</td><td>x</td><td></td><td></td><td>&lt; 3x CO<sub>2</sub></td><td>&gt; 3x CO<sub>2</sub></td></tr></tbody></table>

## 2.2. Simulation selection for inter-model comparison

Apart INMCM, all of the six above models (Tab. 1) provide results with 1x PI and 3x PI CO₂ levels (Tab. 1; except for IPSL which applies 1.5x instead of 1x PI). Therefore, we can apply the one-dimensional energy balance model analyses to these models in order to decompose the warming due to CO₂ level change from 1x to 3x PI. We limit our analyses to simulations with 3x PI CO₂, because simulations with higher CO₂ levels were not available for all six models (Lunt et al., 2021). INMCM only provides a simulation with 6x PI CO₂ level, making energy flux analysis impossible because for one-dimensional energy balance model analyses simulations with at least two CO₂ levels are required (to investigate change in temperature). Therefore, we compare INMCM to the other
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results, where appropriate, but it is not included in the analysis using a one-
dimensional energy balance model.

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2.3. CESM virtual salt formulation

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The CESM1.2 simulations used in this study include with a new representation
of riverine freshwater processes from the ones documented in Zhu et al. (2019,
2020). The ocean model of CESM1.2 uses a virtual-salt formulation with the
surface freshwater flux appearing in the salinity conservation equation as part
of a “virtual salt flux” but not changing ocean volume. The virtual-salt
formulation has a long history in ocean modelling and is known to have large
errors in the salinity simulation near river mouths with large runoff (e.g., Tseng
et al., 2016). As was reported in Zhu et al. (2020), CESM1.2 Eocene simulations
produced negative salinity in the Arctic Ocean due to the difficulty associated
with the default virtual-salt formulation. As a result, Eocene simulations in Zhu
et al. (2019, 2020) used a simple artificial fix, which removes the excessive
surface freshwater over the Arctic and evenly re-distributes it to the global
oceans. This fix removes negative salinity in the Arctic Ocean and has minor
impact on global temperature and salinity distribution, but prevents us from
examining regional processes in the Arctic Ocean. To remedy this, new
CESM1.2 simulations were developed with an updated virtual-salt formulation
(see equation 7 from Tseng et al., 2016). In the updated formulation, local
salinity was used as a reference in the part of virtual salinity flux calculation
associated with river input. The updated formulation is more physical process-
based, largely reduces salinity biases over places with large river runoff, and
therefore has been suggested as a replacement for the default virtual-salt
formulation (Tseng et al., 2016). Data from the improved CESM1.2 simulations
are available in the DeepMIP data archive as version 2, which is what is used
here.

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<figure><img src="image_3.png" /><figcaption>a) 1xPI Percentage of Arctic Ocean covered by sea-ice</figcaption></figure>

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<figure><img src="image_4.png" /><figcaption>b) 3xPI Percentage of Arctic Ocean covered by sea-ice</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>c) 4x and 6xPI Percentage of Arctic Ocean covered by sea-ice</figcaption></figure>

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**Figure 2.** Monthly evolution of sea ice for different models for (a) 1x PI, (b) 3x PI and (c) 4 and 6x PI CO₂ levels.
<figure><img src="image_6.png" /><figcaption>**Figure 3.** DJF sea ice concentration (%) in the northern hemisphere for each model for simulations with 1x (left column) and 3x (right column) CO₂ levels. The yellow line in the simulations with 1x PI CO₂ outlines the Arctic Ocean extent.</figcaption></figure>
## 3. Results

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Figure 2 depicts the sea ice net coverage area seasonal cycle for different CO₂ levels for each model. The threshold for seasonal and perennial sea ice in the Arctic Ocean varies highly across the ensemble (Tab. 1). In Figure 3 we observe that there are large discrepancies between different models in the winter Arctic sea ice response to CO₂ level rise from 1x to 3x PI level. Below we analyse separately the seasonal (section 3.1) and permanent (section 3.2) sea ice presence in the ocean as well as the effect of freezing point temperature (section 3.3) on sea ice formation in the ocean.

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### 3.1 Seasonal sea ice

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Figure 2b shows that, for the Eocene simulations with 3x PI CO₂ level four of six models, with CESM and Hadley as exceptions, simulate seasonal sea ice cover in the open Arctic Ocean. Maximum cover (> 80%) is observed in March and April. All models are nearly ice-free (< ~10%) from July to November. In CESM and Hadley, sea ice (with maximum cover of ~5% and ~15%, respectively) is observed along the coastlines in March and April. Furthermore, in Hadley the 2x PI CO₂ simulation is almost ice free in the Arctic Ocean from August to October (not shown).

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The available simulations with higher (4 and 6x PI) atmospheric CO₂ concentrations (Fig. 2c) indicate an almost ice-free Arctic Ocean in both GFDL simulations. For 4x PI in COSMOS, the sea ice cover is > 40% from February until May, reaching its peak (~ 65%) in April. The Arctic Ocean is almost ice-free (< 10%) in COSMOS from June to December. For INMCM (6x PI CO₂), sea ice cover is > ~50% from December to June and the Arctic Ocean is ice-free (< 10%) from August to October. CESM simulation with 6x PI CO₂ is fully ice free the entire year.

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### 3.2. Perennial sea ice

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For PI CO₂ level, perennial sea ice is seen in all Eocene simulations in the open Arctic Ocean (Fig 2a). However, in some models August and September sea ice is limited to a small area (10-20%) in the ocean. In COSMOS, the ocean is almost covered by sea ice during the entire year while in all other models, sea ice cover drops below 50% in August-September. CESM and Hadley have the least extensive sea ice cover. In the latter, maximum sea ice cover of < ~90% is reached from February to April and the Arctic Ocean is covered in ≤ ~50% from June until November. In CESM, sea ice cover is between 80 and 90% from December until June and in particular in September the sea ice extent is limited
only to the region north of North America. All other models are almost fully covered by sea ice (> 90%) from December until May. The annual evolution of sea ice for 1x PI CO2 in most of the models does not fully resemble the pre-industrial control sea ice seasonal cycle. This is likely due to the different land-sea configuration in the northern polar region for these Eocene 1x PI simulations and actually PI period. In the early Eocene the Arctic Ocean was more isolated from the global ocean than today. Different distribution of land masses around the Arctic Ocean support seasonal changes in the wind directions over the ocean. Seasonal wind reversals were observed around the isolated Arctic Ocean in the late Cretaceous simulations with 4x PI CO2 (Niezgodzki et al., 2019). During the late Cretaceous winter winds blow northward from the North America and Asia over the Arctic Ocean while during the summer southward from the Arctic Ocean (Fig. 8 in Niezgodzki et al., 2019) thus contributing to sea ice formation/retreat. We speculate that seasonal sea ice cycle in the early Eocene Arctic Ocean region resembles more annual sea ice evolution during other deep time period rather than present-day.

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

<figure><img src="image_8.png" /><figcaption>**Figure 4.** TS-diagram of average DJF surface zonal mean sea surface temperatures and salinity in the Arctic Ocean for each model for a) 1x and b) 3x PI CO₂ levels. Degrees represent southernmost latitude of zonal mean temperature and salinity data in the Arctic Ocean. Circles on each line are set every 5° latitudes and on the northernmost latitude (near the North Pole).</figcaption></figure>

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3.3. Freezing point temperature effect on sea ice

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Figure 4 shows the Temperature-Salinity (TS) diagram of DJF zonal surface temperature and salinities in the Arctic Ocean for 1x and 3x PI CO2 levels in each model. For 1x PI most of the profiles are covered by the freezing point temperature lines. For 3x PI, apart from COSMOS, all other profiles are almost entirely above the freezing point temperature. CESM's and Hadley's profile due to greater salinity and temperature (Tab. 2) are clearly located more above the freezing point temperature compared to other profiles. Below we analyse separately two factors influencing TS-profiles.

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Salinity effect

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Tab. 2 (col. 4) shows the width and depth of the Turgai Strait in the narrowest place as calculated on the regular grid interpolated to the 1° resolution using nearest neighbour interpolation in each model. CESM and Hadley have slightly deeper Turgai Strait (Tab. 2; Fig. 1a) than the other models. However, all the depths of the gateway are within the depth of Ekman layer which is ~60 m for the latitudes of 50-55°N (Stärz et al., 2017) where the Turgai Strait is located. The depth of the wind driven layer controls the gateways circulation regime. Therefore, differences in depth of the Turgai Strait between the models should not have a decisive impact on the salinity in the Arctic Ocean. However, in CESM and Hadley, the Turgai Strait is also wider than in other models and the inflow of waters to the strait from the North Atlantic is less inhibited by the presence of islands as compared to other models (Fig. 5). As a result, in both models we observe inflow of waters in a geostrophic regime rather than hydraulically controlled (see Jakobsson et al. (2007) for similar conditions but during the Miocene). The latter occurs when stratified fluid encounters sudden narrowing during the flow what can be observed in the enclosed water basins with estuarine type circulation (Nielsen et al., 2017). The geostrophic regime supports stronger inflow of warmer and saltier waters from North Atlantic through the Turgai Strait and into the Arctic Ocean, thus contributing to warmer and saltier surface waters as compared to other models (Fig. 5). This influx and warmer and saltier waters inhibit sea ice formation in the ocean in CESM and Hadley. Furthermore, inflow of relatively warm waters as compared to the other models through the southern Turgai Strait supports formation of local low-pressure centres over this region in Hadley. As a result, relatively strong easterlies form in the northern part of Turgai Strait which support much stronger northward currents (~1 m/s) as compared to the other models (Fig. 5).
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The implications of above small differences in the width of the Turgai Strait also stress the importance of precise local gateways reconstructions as an input boundary conditions for accurate regional paleoclimate simulations.

Due to model requirements, COSMOS uses a different hydrological discharge model as an input boundary condition (Hagemann & Dümenil, 1998) than provided in Lunt et al. (2017). A different hydrological discharge model might support stronger inflow of freshwater from rivers.
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Sea surface salinity (psu) and surface currents (cm/s) around the Arctic gateways for each 3x Pi simulation. Speed of the currents indicated by length of an arrow at the bottom of each figure.</figcaption></figure>

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<table><thead><tr><th>Model</th><th>DJF global mean surface temperature (°C)</th><th>DJF SST AO (°C)</th><th>DJF SSS AO (psu)</th><th>Width Turgai (km)</th><th>Minimum sill depth in Turgai (m)</th></tr></thead><tbody><tr><td>CESM</td><td>24.36</td><td>3.72</td><td>17.64</td><td>869.28</td><td>45</td></tr><tr><td>COSMOS</td><td>24.25</td><td>-0.01</td><td>1.62</td><td>191.19</td><td>27</td></tr><tr><td>GFDL</td><td>25.41</td><td>0.89</td><td>6.55</td><td>342.03</td><td>35</td></tr><tr><td>Hadley</td><td>24.24</td><td>2.89</td><td>20.41</td><td>457.16</td><td>47.85</td></tr><tr><td>IPSL</td><td>24.37</td><td>0.79</td><td>9.72</td><td>136.81</td><td>35</td></tr><tr><td>MIROC</td><td>22.82</td><td>0.72</td><td>8.40</td><td>200.60</td><td>41.25</td></tr></tbody></table>

**Table 2.** DJF global mean surface temperature, DJF sea surface temperature (SST) and salinity (SSS) in the Arctic Ocean (AO) for simulations with 3x CO₂ levels. Width of the Turgai Strait (Fig. 1a) is in the narrowest location as well as minimum sill depth in Turgai (Fig. 1b) on the grid interpolated to the resolution of 360x180 using nearest neighbour interpolation.

Temperature effect

As shown in Tab. 2, all models apart from MIROC simulate similar DJF global mean surface temperatures for 3x PI CO₂ level. The SST differences between the models in the Arctic region are greater than global surface air temperature differences. To investigate the different DJF temperatures responses to CO₂ level changes over the Arctic region across the model ensemble we utilized a modified one-dimensional energy balance model (see Methods and Tab. 3) (Heinemann et al., 2009; Lunt et al., 2012, 2021).

We split the 2 m DJF temperature warming around the Arctic Ocean due to a CO₂ increase from 1x to 3x PI level into the emissivity component and the combined effect of heat transport convergence plus ocean-atmosphere heat flux. The emissivity can be further separated into the greenhouse effect (including a lapse rate effect) and long wave cloud-induced impacts. Furthermore, we estimate the combined effect of heat transport and ocean - atmosphere heat flux changes on the warming. Table 3 shows the calculated averaged DJF heat flux north of 80° N (approximate area over the Eocene Arctic Ocean) based on the one-dimensional energy balance model. The averaged DJF 2 m temperatures north of 80° N together with differences between the 3x minus 1x simulations for each model are presented for all simulations. The combined effect of heat transport and ocean-atmosphere heat fluxes (col. 8, Tab. 3) should be similar to the temperature differences (col. 4 in Tab. 3) minus total emissivity (col. 7 in Tab. 3). In general, these two heat fluxes should largely balance each other as ocean to atmosphere heat release during winter
in the Arctic region is transported southwards by the winds to the colder surrounding continents. As a result, this combined effect plays a smaller role in the Arctic temperature rise due to CO₂ level rise compared to the emissivity-induced warming.

Tab. 3 (col. 4) shows that the Arctic region in CESM and Hadley experiences the strongest warming, which is > ~4 °C (CESM) and > ~6 °C (Hadley) higher than the warming in other models. This warming is mainly due to the greenhouse effect (col. 6) and associated stronger water vapor feedback under high CO₂ conditions. Furthermore, Hadley experiences also a stronger warming due to ocean to atmosphere heat flux and/or weaker cooling due to atmospheric heat transport. Nevertheless, warming in the Arctic Ocean region as a result of CO₂ level increase is mainly controlled by changes in emissivity via the greenhouse effect in all models. For IPSL, fluxes are weaker due to smaller difference in forcing associated with 1.5x and 3x PI CO₂ reference levels.

The above results have a pronounced effect on the profiles shown at the DJF TS-diagram (Fig. 4). In COSMOS, with freshwater conditions in the Arctic Ocean, the freezing point increases compared to the other profiles, while the higher salinity in CESM and Hadley decreases it. Therefore, salinity plays an important role in the control of Arctic sea ice formation. In CESM and Hadley this effect is enhanced by a stronger temperature response to CO₂ level changes as compared to the other models.

<table><caption>**Table 3.** DJF 2 m temperature for 1x PI (second column) and 3x PI (third column) CO₂ levels. In the fourth column the difference between 3rd and 2nd columns. In the columns 5 to 8 are energy fluxes contributing to the warming</caption><thead><tr><th>Model</th><th>2 m<br>Temp<br>1x PI<br>(°C)</th><th>2 m<br>Temp<br>3x PI<br>(°C)</th><th>Differenc<br>e 2 m<br>temp 3x<br>minus 1x</th><th>Greenhous<br>e<br>Effect<br>(°C)</th><th>Clouds<br>Long wave<br>(°C)</th><th>Total<br>emissivit<br>y<br>warming<br>(°C)</th><th>Ocean<br>heat<br>storage<br>plus<br>atmosph<br>eric heat<br>transport<br>(°C)</th></tr></thead><tbody><tr><td>CESM</td><td>-28.07</td><td>1.75</td><td>29.82</td><td>18.66</td><td>6.82</td><td>25.48</td><td>4.34</td></tr><tr><td>COSMO<br>S</td><td>-35.64</td><td>-10</td><td>25.64</td><td>12.03</td><td>6.01</td><td>18.04</td><td>7.36</td></tr><tr><td>GFDL</td><td>-26.88</td><td>-3.11</td><td>23.77</td><td>14.38</td><td>6.16</td><td>20.54</td><td>3.23</td></tr><tr><td>Hadley</td><td>-31.75</td><td>-0.18</td><td>31.57</td><td>16.09</td><td>5.79</td><td>21.88</td><td>9.69</td></tr><tr><td>IPSL</td><td>-22.88</td><td>-5.3</td><td>17.58</td><td>10.19</td><td>4.28</td><td>14.47</td><td>3.11</td></tr><tr><td>MIROC</td><td>-26.37</td><td>-3.46</td><td>22.91</td><td>14.34</td><td>5.17</td><td>19.51</td><td>3.4</td></tr></tbody></table>
590

due to CO₂ level rise from 1x to 3x PI. Note that in IPSL 1.5x PI CO₂ level was applied instead 1x PI.

592

594

## 4. Discussion

596

Seasonal Arctic sea ice formation during the early Eocene greenhouse is unlikely (Huber and Caballero, 2011; Stein et al., 2015). However, recent data suggest that as early as the middle Eocene the Arctic Ocean was covered by ephemeral sea ice (Darby, 2014) and continental ice was present on Greenland (Tripati & Darby, 2018). The aim of DeepMIP was to simulate the early Eocene climate (~50 Ma) (Lunt et al., 2021); nonetheless, the paleogeography changes are unlikely to have had a decisive effect on sea ice formation as compared to the middle Eocene (~47 Ma) for which sea ice was first documented.

598

600

602

604

Atmospheric CO₂ concentrations in the Eocene would have been an important factor controlling sea ice formation, particularly if the levels were close to the threshold for sea ice formation. The newest proxy-based CO₂ level reconstructions for the early Eocene are lower than previously assumed and are estimated to be in the range of 1170 to 2490 ppmv (Anagnostou et al., 2020).

606

608

610

The CESM and Hadley show the greatest non-linearity (deviation from the logarithmic curve) in surface air temperature rise in response to CO₂ level increase (Lunt et al., 2021). Both models have no sea ice for 3x PI CO₂ and they show the greatest warming of all models (Tab. 3). This pronounced response is linked to feedbacks (greenhouse plus long wave clouds effects) associated with the strongest surface long wave emissivity. An additional factor for the pronounced warming in the Arctic Ocean of the two simulations with 3x PI CO₂ is linked to the relatively vigorous northward flow of warmer waters (of temperature ~7 °C and ~9.5 °C in CESM and Hadley, respectively) via a relatively wide Turgai Strait. However, it should be noted that it is difficult to separate the main driver for sea ice absence from the feedbacks, due their interdependence.

612

614

616

618

620

622

624

Apart from the CESM and Hadley, the threshold for total sea ice disappearance from the Arctic Ocean is > 840 ppmv (Fig. 2). CESM has shown earlier high climate sensitivity and good match with temperature proxy data for the Eocene greenhouse climates (Zhu et al., 2019). Hadley is the only model among CMIP where sea ice was absent in the Arctic Ocean for the last interglacial possibly due to sophisticated sea ice melt point scheme (Guarino et al., 2020). For the IPSL and MIROC we are unable to set the upper threshold because in their

626

628

630
maximum CO₂ level simulations (3xPI) extensive seasonal sea ice is still present (Fig. 2). However, we speculate that the threshold in these two models might be similar to the GFDL where the Arctic Ocean becomes permanently ice free for CO₂ between 3x and 4x PI levels (Fig. 2b,c). All three models simulate a similar evolution of sea ice (Fig. 2b) as well as surface salinity and temperature in the ocean (Fig. 4; Tab. 2) for 3x PI CO₂. A similar result for the Cretaceous can be found in Niezgodzki et al. (2019). They found the threshold for sea ice formation between 3x and 4x PI level in the simulation with closed American gateways and surface salinity in the Arctic Ocean of ~8 psu. A threshold in COSMOS when seasonal sea ice is no longer present in the Arctic Ocean during the Eocene is > 1120 ppmv. This is basically identical to the minimum CO₂ level estimated for early Eocene based on the geological data (Anagnostou et al., 2020). INMCM simulates extensive sea ice during most of the year for 1680 ppmv atmospheric CO₂ concentrations which is within the range of proxy-based CO₂ level estimates. However, INMCM has relatively low climate sensitivity (Zelinka et al., 2020).

Brinkhuis et al. (2006) reconstruct episodic surface freshwater in the Arctic Ocean in the middle Eocene based on the remains of fern Azolla. Furthermore, they speculate that termination of freshwater conditions in the ocean and simultaneous warming by ~3 °C might be related to the influx of water from the adjacent oceanic basins. We speculate that the paleogeography applied in the CESM and Hadley with a stronger water exchange with North Atlantic basin due to wider and slightly deeper Arctic gateways (Tab. 2), might be more suitable to represent termination of the freshwater conditions in the middle Eocene as indicated by Brinkhuis et al. (2006) rather than the early Eocene. Jakobsson et al. (2007) show a similar circulation intensification for the Fram Strait deepening during the Miocene. Apart from the CESM and Hadley, all other models simulate either strong brackish or freshwater (COSMOS) conditions in the surface Arctic Ocean (Tab. 2). The low salinity in the COSMOS supports the most extensive sea ice for 1x and 3x PI CO₂ levels as well as the presence of seasonal sea ice in 4x PI CO₂. These COSMOS results are consistent with earlier simulations of Late Cretaceous climate (Niezgodzki et al., 2019) where seasonal sea ice in the Arctic Ocean was also present for 4x PI CO₂ level in the most enclosed configuration which supported freshwater conditions in the Arctic Ocean. Niezgodzki et al. (2019) also show that the small salinity increase (from ~ 2 psu to ~8 psu) due to gateways opening is sufficient to inhibit sea ice formation in the central Arctic Ocean due to the decrease in the freezing point temperature. Therefore, we speculate that the presence of seasonal sea ice in the Eocene COSMOS 4x PI simulation is possible due to lower salinity than in the other models.
<figure><img src="image_10.png" /><figcaption>**Figure 6.** Simulated DJF surface air temperatures in °C vs. proxy based (red marks) cold month mean temperatures from Huber and Caballero (2011) to the north of 45° N. The temperature ranges (°C) for each mark are shown in the right. Root mean square error (°C) for each simulation shown in the lower right corner of each plot. Solid and dotted violet lines indicate the sea ice line where concentration is > 15 and > 50% respectively.</figcaption></figure>
Proxy-based salinity estimates indicate that the sea surface salinity in the Arctic Ocean was ~18-22 psu during the early Eocene (Pagani et al., 2006), in the range of CESM and Hadley simulated salinities. The strong brackish or freshwater conditions in the Arctic Ocean of other models resemble more middle Eocene freshwater Azolla phase (~50 Ma; Brinkhuis et al., 2006) rather than EECO. However, the surface temperatures of ~10 °C in the Arctic Ocean during Azolla event reconstructed based on the TEX<sub>86</sub> paleothermometer (Brinkhuis et al., 2006) are significantly higher than in these simulations. Furthermore, the reconstructed CMMT for the early Eocene in the northern mid-high latitudes (north of 45° N; Huber and Caballero, 2011) are usually warmer than simulated DJF surface air temperatures (Fig. 6). All temperature estimates are ≥ 0 °C including 3 proxies (2 from North Greenland/Canada and 1 from Greenland-Norwegian Sea) located in the vicinity of simulated sea ice in almost each model using 3x and 4x PI CO<sub>2</sub> levels. These reconstructions rule out sea ice presence in the Arctic Ocean during the early Eocene. Therefore, simulated surface temperatures are too low for the EECO, possibly due to too low CO<sub>2</sub> levels of 3-4x PI applied in the simulations. Changes in the prescribed orbital configurations in the simulations could also reduce the data-model discrepancy. Alternatively, models can miss important processes and/or forcing (such as CH<sub>4</sub>) or the models' sensitivity may be too low. It is also worth mentioning that the simulations where sea ice is absent (CESM and Hadley) have the greatest root mean square error (RMSE; Fig. 6). Most of the temperature reconstructions are terrestrial data that come from outside the Arctic Ocean realm and higher global scale climate sensitivity might not necessarily equate to an absence of sea ice and a better fit to high-latitude land data does not necessarily line up with agreement on sea ice proxies.

In summary, apart from the CESM and Hadley, all models simulate seasonal Arctic sea ice cover when CO₂ levels are at (INMCM) or close to (COSMOS, GFDL, IPSL, MIROC) the range of proxy-based CO₂ estimates for early Eocene. However, there was no sea ice presence in the Arctic Ocean during this time as indicated by geological data (Huber and Caballero, 2011; Stein et al., 2015). This mismatch could indicate that either atmospheric CO₂ concentrations were higher during the early Eocene than applied in these simulations or the models' sensitivities are too low. A decrease in atmospheric CO₂ concentration toward the end of the Eocene (Beerling and Royer, 2011), with simultaneous cooling of sea surface temperatures (Stein et al., 2015) likely triggered seasonal sea ice formation in the Arctic Ocean in the middle Eocene. Narrow and shallow gateways (as in four of six models) between the Arctic and North Atlantic Oceans support limited water exchange between these two basins. As a result, the Arctic Ocean was fresher in these models which could enhance seasonal Arctic sea ice presence during the middle Eocene. We speculate that the CESM
and Hadley simulations are more suitable to represent EECO due to the wider Turgai Strait. It enables more vigorous water exchange between the Arctic and North Atlantic Oceans, thus supporting, in agreement with reconstructions, higher salinity in the Arctic Ocean than in four other models with narrow Turgai Strait. The COSMOS, GFDL, IPSL and MIROC could represent middle Eocene conditions with seasonal sea ice presence in the Arctic Ocean. CO₂ levels in these simulations (of 3-4x PI) might be within the range of the middle Eocene atmospheric CO₂ concentrations (that declined since the end of EECO; Anagnostou et al., 2020). Alternatively, these models could be used to simulate the middle Eocene freshwater Azolla event (although higher CO₂ levels should be applied). CESM and Hadley could represent termination of the Azolla phase.

## 5. Conclusions

We compare simulated sea ice presence in the Arctic Ocean during the greenhouse of the early Eocene using a suite of simulations conducted within DeepMIP (Lunt et al., 2021). Although the simulations provide diverging CO₂ thresholds for seasonal sea ice (from < 840 ppmv to > 1680 ppmv), most of the models simulate sea ice in the central Arctic Ocean for the higher (≥ 840 ppmv) CO₂ levels of the Eocene. The differences in the thresholds result from the different responses of polar winter temperatures to CO₂ increase and salinity differences in the Arctic Ocean across the ensemble. The salinity differences are dominated by width of the gateways between the Arctic and North Atlantic Oceans. Since there are no geological data that support sea ice in the Arctic Ocean before the middle Eocene, the atmospheric CO₂ level during the early Eocene was likely higher than 3x PI CO₂ applied in our simulations or four models in our DeepMIP ensemble (apart from CESM and Hadley) indicate too weak polar sensitivity. The simulations with sea ice presence might be more suitable to represent middle Eocene climatic conditions because declining CO₂ level which started at the end of the EECO could fall in the range of CO₂ levels applied in our simulations. Furthermore, a narrow Turgai Strait in the four models limits the water exchange between the Arctic and North Atlantic Oceans and supports near-freshwater conditions in the Arctic Ocean as reported during middle Eocene Azolla event. We conclude that the exchange between the relatively fresh Arctic Ocean and the more saline North Atlantic Ocean is controlled by the width of the gateway and is thus very sensitive to the model configuration and setup. As a logical next step, we will perform a multi-scale approach (Lohmann et al., 2020) in order to examine the effect of narrow gateways in the framework of global Eocene paleoclimate modelling.
## Acknowledgements

Igor Niezgodzki would like to thank Stefan Hagemann for the preparation of the adjustments of the HD model for COSMOS Eocene setup. Igor Niezgodzki is grateful to the AWI Computing Center for providing the supercomputing resources for carrying out the simulations. Christopher J. Poulsen acknowledges funding through Heising Simon Foundation Award #2016-05 and NSF Award 2002397. Dan Lunt and Seb Steinig acknowledge the NERC SWEET grant, NE/P01903X/1. Agatha de Boer and David Hutchinson acknowledge support from Swedish Research Council projects 2016-03912 and 2020-04791, and FORMAS grant 2018-01621. GFDL numerical simulations were performed by resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre (NSC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977.

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