We use four existing fully coupled simulations generated with CCSM3 : (i) PIlowRes is a preindustrial control simulation and (ii) EEMlowRes is 30 years of output at 125 ka from a transient (130–115 ka) orbitally accelerated Eemian simulation both using the low horizontal resolution of T31 (3.75 ∘) in the atmosphere/land and approximately 3 ∘ grid spacing in the ocean/sea ice component. Furthermore, we analyze (iii) PIhighRes, a preindustrial control simulation, and (iv) EEMhighRes, an Eemian simulation with perpetual 125 ka forcing both at a resolution of T85 (1.4 ∘) in the atmosphere/land and approximately 1 ∘ in the ocean/sea ice . Hence, we can compute two realizations of the Eemian minus preindustrial climate anomaly (denoted as EEM-PIlowRes and EEM-PIhighRes) based on the same CCSM3 model but differing in horizontal resolution. Note that the two sets of EEM-PI realizations also used slightly different values for GHG concentrations and solar constant and that the transient character of EEMlowRes is different from the time-slice approach of EEMhighRes.

Additionally, a set of simulations is generated employing CCSM4 at a resolution of 0.9 ∘ × 1.25 ∘  in the atmosphere and land surface with prescribed time-varying monthly SSTs and sea ice cover. This CCSM4 setup is termed atmosphere–land-only and comprises the Community Atmosphere Model version 4 CAM4; and the Community Land Model version 4 but no dynamic representation of the ocean and sea ice. Besides the benefit of being computationally cost-efficient compared to fully coupled simulations, this setup is convenient for sea ice sensitivity experiments, as one can simply compute the atmospheric response to any prescribed change in sea ice (and SSTs). As a drawback, these simulations do not allow feedbacks with the ocean and sea ice components. A general model validation of the CCSM4 atmosphere–land-only setup is given by .

In total, we perform 12 simulations with CCSM4, of which the 6 simulations listed in Table  build the core of this study, whereas the remainder of the simulations will be shortly discussed in Sect. . Each simulation has a length of 30 years plus a 3-year spin-up phase, and the external forcing is held constant throughout the simulation.

Both models, CCSM3 and CCSM4, are widely used in the climate science community and have been thoroughly validated for present-day climate conditions e.g.,. When comparing the high- and low-resolution versions of CCSM3, the latter is generally found to have a stronger cold bias in the North Atlantic related to underestimated ocean heat transport and excessive Arctic sea ice . However, illustrate (their Fig. 4) that indeed both model versions rather underestimate SATs in the North Atlantic sector for preindustrial conditions. In the successor model, CCSM4, these biases have been substantially improved through changes in sea ice albedo and ocean overflow parameterizations . Further, CCSM4 shows in general good skill in simulating the present-day surface climate and atmospheric circulation in and around Greenland . Hence, we have good confidence in CCSM4's capability in representing the components of the North Atlantic and Greenland climate system that are of importance for this study, e.g., SAT, surface energy fluxes, surface winds, or precipitation.

The CCSM3 has further been used for a number of simulations of the Eemian interglacial and respective comparisons with Eemian proxy records e.g.,. Rather than looking at absolute Eemian climate conditions, we compare models and proxies based on their EEM-PI climate anomaly. Comparing the change in Eemian climate with preindustrial values avoids possible caveats associated with mean climate model biases and the calibration of proxies to an absolute level. Equivalently, we focus in this study on the simulated EEM-PI climate anomaly to quantify the Eemian state of any target climate variable. More precisely, we define a set of climate anomalies listed in Table . Based on the CCSM3 simulations we compute EEM-PIlowRes and EEM-PIhighRes differing in horizontal resolution as well as other minor settings as explained in Sect. . Similarly, we calculate EEM-PI1 and EEM-PI2, which are based on the same atmosphere–land-only CCSM4 setup but differ with respect to the origin of the prescribed lower boundaries (either CCSM3lowRes or CCSM3highRes). The difference between these two last EEM-PI anomalies themselves is referred to as EEM-PIdiff, which represents the climate response related to the spread/uncertainty in the EEM-PI sea ice and SST changes. In addition, we use the terms “LabS-shift” and “NordS-shift” for the comparison of the EEM experiments including a regional shift in lower boundary conditions compared to the reference experiment (i.e., the situation before the sea ice shift).

The first part of our analysis assesses the uncertainty of the Eemian warming as suggested by the spread among state-of-the-art climate models. This relates to the model-intercomparison study by , which showed that the EEM-PI annual mean atmospheric warming (their Fig. 5) strongly varies among different models and even applies to two EEM-PI simulations with the same climate model but different model versions (denoted as CCSM3_Bremen and CCSM3_NCAR therein). Here we show an analogous comparison of the EEM-PI temperature response of two versions (EEM-PIhighRes, Fig. a) and EEM-PIlowRes, Fig. c) of fully coupled CCSM3 simulations.

CCSM3 EEM-PIhighRes exhibits a distinct warming in the NH high latitudes, with the strongest signal occurring in an area including the Arctic, Greenland, and the North Atlantic (Fig. a). Significant warming but of smaller magnitude is further found in Europe and most of North America. In contrast, the CCSM3 EEM-PIlowRes warming is very limited in terms of magnitude and spatial expansion (Fig. c). In fact, large areas of the NH experience an annual mean cooling. The difference between the two EEM-PI warming patterns (Fig. e) illustrates a stronger warming of EEM-PIhighRes than EEM-PIlowRes in almost the entire NH, but most distinctively over the Arctic and the North Atlantic ocean.

The main reason for the remarkable discrepancy in EEM-PI warming among the two pairs of CCSM3 simulations is likely the different horizontal resolution as it has been shown that the low- and high-resolution versions of CCSM3 show distinct differences for various climatic features even under present-day conditions . Moreover, the two sets of simulations do not include identical GHG and solar forcing for either the PI or the EEM simulations : CCSM3 EEMhighRes includes a slight increase in the solar constant and the N2O concentration with respect to CCSM3 PIhighRes. In contrast, CCSM3 EEMlowRes uses the same solar constant but consistently lower GHG concentrations than CCSM3 PIlowRes. Hence, slight differences in the prescribed external forcing may also contribute to the spread in the EEM-PI warming pattern, here as well as in .

In the next step, we aim to link the discrepancy in EEM-PI temperature response among the two CCSM3 versions (discussed in Sect. ) with the models' representation of SSTs and sea ice in the North Atlantic sector. For consistency this evaluation is done with one single model (i.e., the atmosphere–land-only CCSM4) using the SSTs and sea ice of both preindustrial and both Eemian fully coupled CCSM3 simulations as boundary conditions (see Sect.  for details on the model setup).

Comparing the CCSM4 simulations with their CCSM3 equivalents, we find high similarity: the CCSM4 EEM-PI2 (Fig. b) largely exhibits the same distinct high-latitude warming as its CCSM3 counterpart (EEM-PIhighRes, Fig. a), and there is also high agreement for EEM-PI1 and EEM-PIlowRes (Fig. c and d). Eventually, the CCSM4 EEM-PIdiff SAT pattern (Fig. f) strongly suggests that large parts of the spread between the two diverse fully coupled CCSM3 EEM-PI responses (shown in Fig. e) originate from differences in SSTs and sea ice. Note that the two pairs of CCSM4 simulations (i.e., PI1, PI2 and EEM1, EEM2) use identical experimental setups (see Table 1), so the CCSM4 EEM-PIdiff pattern (Fig. f) necessarily results from differences in the prescribed SSTs and sea ice. The strongest impact of the lower boundary conditions is simulated for the area around Greenland, but the warming extends throughout most of the NH extratropics. In contrast, the influence of the lower boundary conditions on low-latitude regions is smaller in magnitude. In the following we will focus on the distinct EEM-PIdiff SAT signal in the Greenland/North Atlantic region and analyze in detail its relationship with the underlying sea ice cover and SSTs.

The EEM-PI change in SSTs and sea ice simulated by the two fully coupled CCSM3 simulations is shown in Fig. . EEM-PIhighRes shows a warming of the North Atlantic and a retreat of the sea ice cover in all seasons. In winter (DJF) and spring (MAM), the main reduction in sea ice is confined to the Labrador Sea, whereas in summer and autumn the sea ice cover in the Nordic Seas is reduced as well. The strongest increase in SSTs (>4 ∘C anomaly) is found south of Greenland, corresponding to a strengthening of the Atlantic subpolar gyre that fosters convection of relatively warm subsurface water. A strong subpolar gyre during the Eemian induced by decreased sea ice export from the Arctic is in agreement with previously published results based on two different climate models and marine sediment proxies .

The EEM-PIlowRes change in SSTs and sea ice (Fig. , bottom row) deviates from EEM-PIhighRes. In fact, the North Atlantic mostly cools and even the high levels of summer insolation during the Eemian only result in a moderate surface warming in shallow coastal waters. In the Nordic Seas, the summer SSTs even decrease and the sea ice cover is expanded during the Eemian compared to the preindustrial climate throughout the year. This likely relates to a relatively weak Atlantic meridional overturning circulation (AMOC) in low resolution during the Eemian compared to present day .

The diverging oceanic responses among the two versions of the fully coupled CCSM3 to the Eemian external forcing are likely connected to intermodel differences in the mean ocean state for present-day conditions and hence linked to the model biases. Note that, for present day, the low-resolution CCSM3 already exhibits too weak an AMOC and, consequently, an underestimated heat transport to the North Atlantic that fosters a large sea ice cover . In the present-day high-resolution CCSM3, the AMOC is stronger and the NH sea ice cover is smaller, which is closer to observations. However, the high-resolution CCSM3 still has a pronounced cold bias in the subpolar North Atlantic related to an underestimated subpolar gyre, which itself is a consequence of biases in the surface wind forcing . As described above, the subpolar gyre seems to strengthen for Eemian climate conditions in the high-resolution CCSM3, causing warmer SSTs and a reduced sea ice cover in many areas of the North Atlantic (Fig. , top row). In contrast, the overestimation of the NH sea ice in the low-resolution CCSM3 likely generates North Atlantic conditions that prevent an Eemian strengthening of the subpolar gyre due to the non-linear character of the gyre dynamics and its strong dependence on the background salinity and thus freshwater fluxes linked to sea ice processes . Consequently, we are missing a respective warming of the North Atlantic in EEM-PIlowRes (Fig. , bottom row).

When using these CCSM3 sea ice and SSTs as prescribed lower boundary conditions for the CCSM4 atmosphere–land-only simulations, the distinct differences in the EEM-PI changes in terms of lower boundary conditions directly translate into respective responses in the CCSM4 atmospheric temperature (compare Figs.  and ). As expected, the influence of sea ice and SSTs is particularly strong on SATs above oceanic grid cells; for instance, any EEM-PI cooling or warming in SSTs can be identified in the EEM-PI SAT response. For example, in Eemian winters the decreased solar insolation leads to a widespread atmospheric cooling, but in EEM-PI2 (Fig. , top left) the direct effect of the external forcing on SATs is superimposed on the North Atlantic domain by oceanic changes showing a warming (Fig. , top left). Consequently, we find clear differences between the EEM-PI1 and EEM-PI2 warming in both annual mean (Fig. ) and seasonal mean (Fig. ) SATs. The strongest seasonal differences in SATs as a result of diverging lower boundary conditions is found for DJF and MAM (see Fig. , bottom row). In these two seasons, the EEM-PIdiff warming is not restricted to oceanic areas but also includes substantial changes in Greenland's SATs. In contrast, the differences in lower boundary conditions scarcely lead to a diverse warming outside of the North Atlantic domain during summer.

In summary, we have demonstrated that distinct differences in the simulated Eemian warming based on fully coupled models are explained by their differences in sea ice and SSTs. The influence of the sea ice cover and the surface ocean on the EEM-PI atmospheric response is particularly strong in the North Atlantic and apparent in all four seasons but especially in winter. In the following, we focus on winter and analyze the processes that are responsible to transmit changes in sea ice/SSTs to the atmosphere. Furthermore, we study atmospheric transport processes which control how the available heat in the atmosphere is spatially distributed. Eventually, the seasonality of key processes is presented in Sect. .

The distinct EEM-PIdiff warming (Fig. , bottom row) needs to be understood as an additional Eemian warming caused by the prescribed CCSM3highRes SSTs and sea ice with respect to the CCSM3lowRes boundary conditions. This effect is unrelated to the direct atmospheric response to the Eemian external forcing. Consequently, the EEM-PIdiff warming requires oceanic heat sources, i.e., an increased heat transfer from the surface ocean to the atmosphere. Two types of heat sources are possible: either a warmer surface ocean, which directly warms the overlying atmosphere, or a reduction in the sea ice cover, which exposes a relatively cold atmosphere to the underlying (warmer) ocean surface. In order to assess these two processes for winter, we compare the DJF EEM-PIdiff SST and SIC anomalies with the response of the atmospheric surface energy fluxes (Fig. ). All surface energy fluxes are defined positive in the upward direction – i.e., a positive flux is warming the overlying atmosphere.

The comparison of the SST/SIC map (Fig. a) with the net surface energy flux response (Qnet, Fig. b) reveals that most of the warmer North Atlantic acts as a heat source. Qnet is defined here as the sum of sensible heat, latent heat and longwave radiation. We omit the shortwave component in the calculation of Qnet because increased downward shortwave radiation resulting from modifications in surface albedo, e.g., by changing an ocean grid cell from ice-covered to ice-free, does not warm the atmosphere directly but warms the ocean, an effect that is suppressed in our experimental setup, where SSTs are prescribed.

The strongest positive Qnet anomaly is confined to the areas of sea ice retreat in the Labrador Sea, the East Greenland Current south of Denmark Strait, and the northern Nordic Seas. The dominant components of Qnet are the turbulent energy fluxes (sensible and latent heat, Fig. c, d), which show an increase of up to 150 W m-2. In contrast, the radiative fluxes (10–20 W m-2 increase) are of second-order importance. This result is in agreement with previous sea ice sensitivity experiments e.g.,. In fact, the DJF net longwave radiation slightly increases over the warming North Atlantic (not shown), whereas shortwave radiation is mostly absent in the high-latitude NH during winter. The turbulent energy fluxes (Fig. c, d) show negative responses in areas adjacent to sea ice loss and therefore adjacent to the regions with the strongest positive energy flux responses. The resulting dipole patterns can be understood by considering that the positive fluxes locally warm the low-level atmosphere, and this heat can be transported to areas nearby. The warmer air masses then lose some of their excess heat to the underlying ocean, resulting in negative heat fluxes. Hence, the SSTs would rise in regions with negative flux responses, and eventually this would dampen the negative fluxes by reducing the air–ocean temperature difference. However, as SSTs are prescribed in our CCSM4 simulations, this negative feedback is suppressed and consequently the dipoles in turbulent energy flux responses are rather pronounced. Nevertheless, similar dipole features were also identified in fully coupled model simulations as well as in atmospheric reanalyses and thus are only partly due to our experimental setup.

In summary, the DJF EEM-PIdiff differences in terms of SSTs and SICs lead to several distinct oceanic heat source areas in the North Atlantic, whereof the areas marked by a sea ice retreat are strongest, as indicated by the maximum in (upward) surface energy flux anomalies (Fig. b–d).

To understand the SAT response of the two sea ice shift experiments, we consider the atmospheric heat budget. The heat budget is based on the thermodynamic energy equation (TEE) in which the conservation of energy is applied to a moving fluid : δTδt=-v×∇T-δTδpω+αcpω+Jcp. The terms of the TEE consist of the horizontal (-v×∇T) and vertical (-δTδpω) heat temperature advection, the adiabatic compression (αcpω) resulting from a vertical displacement of an air parcel, and diabatic processes (Jcp) such as radiative or latent heating. Within the CAM4 model the heat budget is calculated considering modifications to the TEE as the physical principles are employed in a numerical modeling framework and certain processes need to be parameterized. For example, turbulence in the atmospheric boundary level is not resolved and consequently this transport is parameterized. Taking this into account, we use the simplified description of the CAM4 heat budget: δTδt=HTres+HTpar+Jcp.

In Eq. (2) the first three terms of the right-hand side of the TEE (Eq. 1) are replaced with the heat transport resolved within the CAM4 dynamical core (HTres) and the heat transport due to parameterized processes (HTpar). The latter mainly represents vertical heat transport due to sub-grid eddies. Note that all simulations are run into equilibrium, so the total temperature tendency (δT/δt) is zero.

The CAM4 heat budget response for both sea ice shift experiments is shown in Fig.  for the lowest terrain-following level. The heat budget response to the LabS-shift experiment (Fig. a–c) indicates that, over the Labrador Sea/Baffin Bay area, HTpar is the dominant process to vertically transport heat from the ocean surface to the overlying low-level atmosphere. In contrast, HTres is responsible for carrying the excess heat away from the heat source area. This heat mainly accumulates in the North Atlantic area located south of Greenland, where it is vertically mixed to the surface by sub-grid eddies (measured by HTpar) and, eventually, negative heat flux anomalies (Fig. b) that transfer the energy excess out of the atmosphere into the ocean. Furthermore, the warming in western Greenland (Fig. a) is related to enhanced HTpar (Fig. b).

The response of the CAM4 heat budget to the NordS-shift is shown in Fig. d–f. Similarly to the LabS-shift experiment, the heat generated by the positive Qnet anomalies in the NordS sea ice retreat area (Fig. f) is vertically transported to the overlying atmosphere by HTpar. Further, HTres is responsible for horizontally distributing the heat to the North Atlantic southwest of the sea ice retreat area. There, the excess heat is transported back down to the ocean surface by turbulent eddies (indicated as negative HTpar anomaly, Fig. f) and is eventually lost to the ocean as revealed by negative Qnet anomalies (Fig. f). In contrast to the LabS-shift experiment, however, the sea ice retreat in the NordS also leads to distinct heat budget changes over Greenland (Fig. ). Depending on the Greenland region, the low-level warming is caused by either enhancement of the resolved (HTres) or the parameterized (HTpar) heat transport (Fig. d, e). In contrast, diabatic processes are of secondary importance for explaining the spatial distribution of the heat released in the NordS source region (Fig. f). Above Greenland, the NordS-shift experiment mostly leads to a decrease in diabatic heating at low levels (Fig. e), whereas the diabatic heating increases in the same areas at higher levels (not shown). This is explained by the fact that, as atmospheric temperatures rise above Greenland (see Fig. b), condensation of moisture is vertically shifted to higher atmospheric levels. In general, most of the diabatic heating response in both shift experiments (Fig. c, f) can be attributed to changes in latent heating rather than radiative processes. Thus, the response of the cloud cover (which alters the radiation budget) to either sea ice perturbation is small and negligible (not shown).

Consequently, we find that moisture- and radiation-related processes are not of high relevance in explaining the presence (absence) of a warming in Greenland in the NordS-shift (LabS-shift) experiment shown in Fig. . Instead, the warming in Greenland in the NordS-shift experiment is related to heat advection as suggested by the two heat transport terms (Fig. d, e). Theoretically, Greenland's warming can be caused by either direct advection of the heat from the heat source (i.e., the sea ice retreat area) or changing the dynamics of the atmospheric flow above Greenland. Whereas the first process alters heat advection by changing temperature gradients, the latter has an impact on heat advection by changing the flow itself. In order to analyze these processes in detail, we consider the low-level winds in and around Greenland (Fig. ).

The atmospheric circulation in the NH during Eemian winters is similar to present-day winters . The dominant circulation in Greenland is a stationary high-pressure system, known as the Greenland anticyclone . Accordingly, Greenland's wind field in the lower troposphere is characterized by strong winds that encircle Greenland clockwise, whereas vertical winds indicate subsidence above the margins of the Greenland ice sheet (Fig. a). The Greenland anticyclone can hence be regarded as an isolated wind system that hinders the exchange of heat and moisture between Greenland and adjacent areas. In the case of the LabS-shift experiment, the warming in the LabS area scarcely leads to enhanced heat advection to Greenland because the winter mean winds do not point towards Greenland but rather to the North Atlantic areas located southeast (see vectors in Fig. a). There, enhanced heat advection is found based on the heat budget calculation (Fig. a), causing a local warming (Fig. a). The dynamic response of the winds in the LabS-shift experiment (Fig. b) even shows an intensification of the northwesterly winds in the LabS area and implies an additional strengthening of the heat advection in the southeasterly direction. In contrast, the low-level winds hardly change above Greenland and thus there is also no dynamic response of the atmospheric flow in the LabS-shift experiment that would result in a significant temperature response in Greenland.

The Greenland anticyclone also acts as a barrier for heat approaching Greenland from the NordS area. The low-level winds east of Greenland indicate southward flow along Greenland's east coast (Fig. a) which further relates to the Iceland low-pressure system. Consequently, the winter mean circulation transports heat released in the NordS domain southwards along Greenland's coast and hence there is no direct heat transport towards central Greenland. However, the NordS-shift experiment shows distinct modifications to the low-level winds in and around Greenland (Fig. c): there is strong anomalous flow towards central Greenland from the North Atlantic area located to the southeast. More precisely, the shallow baroclinic response to the strong surface warming east of Greenland (Fig. b) leads to a surface pressure reduction over southern Greenland (not shown) and the corresponding anomalous low-level flow shown in Fig. c. Hence, the NordS-shift is able to substantially weaken the barrier effect of the Greenland anticyclone, so that warm air masses can enter Greenland. Accordingly, the vertical winds in Fig. c show anomalous upward motion in southeastern Greenland as the onshore winds are lifted over the steep margins of the ice sheet.

In summary, the sea ice perturbation of the NordS-shift experiment is able to substantially alter the atmospheric flow above Greenland leading to a change in heat transport (as indicated by the HTres and HTres anomalies in Fig. d, e). This, eventually, is responsible for the large-scale warming seen in Fig. b. In contrast, the dynamic response to the LabS-shift does not foster anomalous heat advection towards Greenland, and thus the Greenland SAT response in this experiment is very limited (see Fig. a).

Despite the result that moisture-related processes are not of high importance to explain the warming in either sea ice experiment (as explained in Sect. ), the response of the hydrological cycle to the sea ice perturbations is substantial (Fig. ). Changes in the hydrological cycle are described in terms of the atmospheric moisture budget, which states that any change in moisture accumulation, defined as precipitation minus evaporation (P-E), must be compensated for by moisture advection. The latter is calculated as the convergence of the vertically integrated zonal and meridional moisture fluxes. This calculation is based on daily model output using finite differences.

The LabS-shift response in P-E shows that, in the LabS area, evaporation dominates over a concurrent precipitation increase (Fig. a). Hence, the sea ice retreat area acts as an atmospheric moisture source in addition to its role as a heat source. The excess moisture is mainly transported eastwards (Fig. b) and deposited either in the North Atlantic in the southeast or in western Greenland. While the eastward transport roughly corresponds to the winter mean circulation indicated by the horizontal winds in Fig. a, the moisture advection to Greenland is due to synoptic systems (i.e., cyclones) that occasionally transport substantial amounts of moisture northwards along Greenland's west coast .

The response of the hydrological cycle to the sea ice shift in the NordS exhibits similar changes: in the areas of sea ice reduction, increased evaporation (as also apparent in the latent heat flux, Fig. h) dominates over precipitation changes leading to distinctively negative P-E anomalies (Fig. c). On the other hand, positive P-E anomalies and hence increased moisture deposition are simulated for other areas in the North Atlantic and in Greenland related to corresponding changes in moisture advection (Fig. d). For Greenland, most of the additionally available moisture precipitates above the steep margins of the ice sheet in the southeast, where the moist air masses are lifted and, consequently, cause orographic precipitation. The resulting maximum in winter precipitation in southeastern Greenland is a prominent feature in the North Atlantic winter climate e.g., related to a local maximum in cyclone frequency in the area of the Icelandic low. Enhanced moisture availability in the NordS domain thus results in a precipitation increase in this specific Greenland region, with cyclones being the carrier. Moreover, increased precipitation in southeastern Greenland relates to the previous result of an enhancement of the onshore winds in response to the NordS-shift (Fig. c). Hence, the dynamic response itself fosters the advection of both heat and moisture from the Nordic Seas towards eastern Greenland.

The results presented so far show a distinct impact of regional sea ice reductions on the winter climate in the North Atlantic sector. To assess the importance of changes in sea ice cover for the interpretation of Eemian climate proxy records, which mostly reflect annual mean changes, the temporal scope will be broadened to the other seasons. In the following, we analyze the relationship between the seasonality in sea ice reduction and the seasonality of the atmospheric response. For this purpose, we compute the annual cycles of the area-averaged SIC, Qnet, and SAT anomalies for the LabS domain (Fig. a, c) and the NordS domain (Fig. b, d). These domains are defined in Fig. a and b.

The average monthly SIC reduction in the LabS domain as a result of the idealized LabS-shift varies between 10 and 20 % reduction (Fig. a). As previously discussed, a certain retreat in the sea ice cover reflects a change in lower boundary conditions to the atmosphere influencing the exchange of heat and moisture at the ocean–atmosphere interface. Hence, in terms of energy, the sea ice retreat is transferred to the overlying atmosphere by anomalous net surface energy fluxes (Qnet in Fig. a). The LabS-shift results in a distinct annual cycle of the Qnet response with the maximum increase during winter in contrast to almost no change in summer. Hence, the magnitude of the Qnet response is not tied to the concurrent SIC reduction but rather to seasonally diverse climate conditions. More precisely, we find a winter maximum in the turbulent (i.e., sensible and latent) heat flux response arising from the fact that this is the time of year when the low-level air temperatures are coolest relative to the underlying surface (sea ice or open water). Consequently, a sea ice retreat that exposes SSTs to the overlying atmosphere has a distinct “heat source effect” in the winter half-year. In contrast, this is effect is reduced in summer, when atmospheric and surface ocean temperatures are comparable. This seasonally diverse behavior of the heat flux response to changes in sea ice is well known and has previously been identified in model and reanalysis studies investigating recent and future Arctic sea ice changes . As expected, an increase in the net energy flux directly translates into a local SAT signal, and thus the annual cycles of Qnet and SAT strongly resemble each other (Fig. a). Accordingly, the maximum SAT response in the LabS domain emerges in winter (>5 ∘C) coinciding with the Qnet maximum. Conversely, the summer warming is smaller in magnitude (∼1 ∘C).

Equivalently to the LabS-shift experiment, the NordS-shift results in a SIC reduction in the range of 10–15 % throughout the year (Fig. b). However, the Qnet response to the NordS-shift lacks the winter maximum previously found for the LabS-shift (compare Fig. a and b). This is explained by the dipole effect in turbulent heat fluxes (see Fig. g, h): the strongly positive heat flux anomalies in the sea ice retreat areas are partly offset by negative anomalies in adjacent areas when averaging across the NordS domain (as done in Fig. b). In contrast, in the LabS-shift experiment the negative part of the heat flux dipole is located outside of the LabS domain (see Fig. c, d) and hence not considered in the calculation of the Qnet values shown in Fig. a. Nevertheless, the seasonality of the SAT response to the NordS-shift (Fig. b) is similar to the LabS-shift experiment, with a winter maximum and a summer minimum, respectively.

In summary, we find that a sea ice retreat substantially influences the local winter climate in both regions, whereas the response of the summer climate is smaller in amplitude. The same result is true for EEM-PIdiff (Table ), as shown by the annual cycles in Fig. c and d. Hence, although EEM-PIdiff exhibits a sea ice reduction in any season and not mostly distinctively in winter, the Qnet and SAT response is largest in the cold season. In the LabS domain, the winter sea ice reduction corresponding to EEM-PIdiff is considerably smaller than for the LabS-shift experiment (compare Fig. a and c) and, accordingly, the Qnet and SAT maxima in winter are less distinct. On the other hand, the EEM-PIdiff SIC reduction in the NordS domain during winter is in the same range as in the NordS-shift experiment (compare Fig. b and d), so the respective SAT responses are similar in magnitude as well. The comparability of the results of EEM-PIdiff and the two shift experiments further illustrates the utility of the idealized sea ice sensitivity experiments for identifying the impact of regional sea ice changes on the Eemian climate.

In addition to the seasonality of sea ice changes and its response on the overlying atmosphere, we assess the annual cycle in Greenland's SAT response (Fig. e). In contrast to the SAT response in the area of sea ice perturbation (i.e., the LabS or NordS), which is the direct result of altered surface energy fluxes, a change in Greenland temperatures additionally requires anomalous heat transport (as discussed in Sect. ). The EEM-PIdiff Greenland SAT response shows a distinct warming in winter/spring but only a moderate warming during the warm season. Furthermore, the NordS-shift results in a very similar warming response to EEM-PIdiff, confirming the previous result that a sea ice retreat in the NordS is crucial to explain the widespread warming seen in EEM-PIdiff (Fig.  bottom row). In contrast, the sea ice perturbation caused by the LabS-shift only leads to a very moderate Greenland warming. Hence, despite the distinct local winter warming caused by the LabS-shift (Fig. a) the absence of a heat transport towards Greenland prevents a concurrent Greenland warming in any season (Fig. e).

As introduced in Sect. , we perform additional sea ice sensitivity experiments in which we test modifications to the sea ice shift approach. The results of these simulations with respect to the SAT response in the area of sea ice perturbation (LabS or NordS) as well as in Greenland are listed in Table .

In EEMLabS2 and EEMNordS2 we use the EEM2 lower boundary conditions as a baseline to apply the shift instead of those of EEM1 used so far for EEMLabS and EEMNordS. Figure  shows that the position of the Eemian sea ice edge in EEM1 differs from EEM2. Applying the shift to the latter thus results in a change of the location of the sea ice anomalies and hence in the location of the strongest heat flux anomalies (i.e., the heat source). In EEMLabS2 and EEMNordS2 the resulting heat source regions are shifted northwards with respect to EEMLabS and EEMNordS. Comparing the temperature response in Table  of EEMLabS2/EEMLabS and EEMNordS2/EEMNordS, we find that shifting the position of the heat source area only has a moderate effect on the local warming as well as on the response in Greenland. Still, a northward shift of the heat source area seems to reduce the magnitude of warming.

Moreover, we generate four sensitivity experiments for which we shift the sea ice but not the SSTs in order to exclude the response to a (possibly overestimated) surface ocean warming that comes along with the SST shift. Accordingly, in these simulations (denoted with an ICE suffix in Table ) the heat source is restricted to the area of sea ice retreat as the SST anomalies shown in Fig. a and e are omitted. In the LabS region this model setup appears to be of minor importance as the warming response in both ICE simulations does not deviate from the response in the experiments including the SST shift. In contrast, the effect is much larger for the NordS-shift experiment, where ignoring the widespread SST increase (shown in Fig. e) substantially reduces the strength of the heat source. Consequently, the ICE simulations generate a smaller temperature response compared to the simulations including the SST shift.

Our whole set of sensitivity experiments covers a reasonable range of possible sea ice (and related SST) changes in the two target regions. The following results are robust among all simulations as shown in Table . (i) A sea ice reduction in the LabS domain leads to a strong local warming (DJF: 5.3–6.0 ∘C; annual: 2.3–3.6 ∘C). (ii) The response in Greenland temperature to a perturbation in the LabS is limited due to the lack of heat transport towards Greenland. The annual mean Greenland SAT increase of 0.4–0.5 ∘C is still a significant warming but mostly reflects the warming in western Greenland shown in Fig. a. (iii) In the NordS region the strength of the heat source depends on the specific experimental setting (i.e., inclusion/exclusion of SST changes, location of the perturbation). This results in a considerable spread in the NordS temperature response (DJF: 2.3–4.6 ∘C; annual: 1.2–3.1 ∘C). (iv) Correspondingly, there is a spread in terms of warming in Greenland (DJF: 1.1–3.8 ∘C; annual: 0.6-2.1 ∘C) depending on the strength of the heat source in the NordS. (v) The impact of the NordS-shift on the Greenland SAT exceeds the influence of the LabS-shift in all cases considered here.

As a next step, we compare the temperature responses of the sensitivity experiments with EEM-PIdiff (Table ). Note that EEM-PIdiff (defined in Table ) indicates the temperature response resulting from the uncertainty in EEM-PI changes in the lower boundary conditions based on two pairs of fully coupled CCSM3 simulations. The EEM-PIdiff temperature signal in the LabS region is below the range of the sensitivity simulations, implying that the heat source employed in the idealized LabS-shift experiments is rather overestimated. In contrast, the EEM-PIdiff warming in the NordS area conforms to the idealized experiment featuring the strongest heat source in the NordS (i.e., EEMNordS). Hence, the idealized scenario of a distinct sea ice reduction and surface warming included in EEMNordS (Fig. e) is in agreement with EEM-PI changes simulated by state-of-the-art climate models.