Arctic climate and its interaction with lower latitudes under different levels of anthropogenic warming in a global coupled climate model
- First Online:
- Cite this article as:
- Koenigk, T. & Brodeau, L. Clim Dyn (2016). doi:10.1007/s00382-016-3354-6
Three quasi-equilibrium simulations using constant greenhouse gas forcing corresponding to years 2000, 2015 and 2030 have been performed with the global coupled model EC-Earth in order to analyze the Arctic climate and interactions with lower latitudes under different levels of anthropogenic warming. The model simulations indicate an accelerated warming and ice extent reduction in the Arctic between the year-2030 and year-2015 simulations compared to the change between the year-2015 and year-2000 simulations. Both Arctic warming and sea ice reduction are closely linked to the increase of ocean heat transport into the Arctic, particularly through the Barents Sea Opening. Decadal variations of Arctic sea ice extent and ice volume are of the same order of magnitude as the observed ice extent reductions in the last 30 years and are dominated by the variability of the ocean heat transports through the Barents Sea Opening and the Bering Strait. Despite a general warming of mid and high northern latitudes, a substantial cooling is found in the subpolar gyre of the North Atlantic under year-2015 and year-2030 conditions. This cooling is related to a strong reduction in the AMOC, itself due to reduced deep water formation in the Labrador Sea. The observed trend towards a more negative phase of the North Atlantic Oscillation (NAO) and the observed linkage between autumn Arctic ice variations and NAO are reproduced in our model simulations for selected 30-year periods but are not robust over longer time periods. This indicates that the observed linkages between ice and NAO might not be robust in reality either, and that the observational time period is still too short to reliably separate the trend from the natural variability.
KeywordsArctic climate change Climate variability Global climate modelling Coupled simulations Arctic–lower latitude interactions
Observations of the last decades indicated that Arctic near surface temperature trends were about twice the rate of the global mean warming in the last decades (Stocker et al. 2013; Richter-Menge and Jeffries 2011). Different processes have been suggested to contribute to this amplified warming signal including the ice albedo feedback (Serreze et al. 2009; Screen and Simmonds 2010a, b), changes in clouds and water vapour (Graversen and Wang 2009; Liu et al. 2008), enhanced meridional energy transport in the atmosphere (Graversen et al. 2008) and the ocean (Spielhagen et al. 2011; Koenigk and Brodeau 2014), vertical mixing in Arctic winter inversion (Bintanja et al. 2011) and temperature feedbacks (Pithan and Mauritsen 2014). Sea ice cover and volume have dramatically been reduced in the last decades (Comiso et al. 2008; Devasthale et al. 2013) and also land snow cover has been subject to changes with an earlier onset of the snow melt and reduction of summer snow extent (Brown and Robinson 2011).
Variations of snow and ice cover have a large influence on local and remote climate conditions (Magnusdottir et al. 2004; Alexander et al. 2004; Kvamstö et al. 2004; Koenigk et al. 2009) and a number of recent studies suggested linkages between the recent sea ice loss and mid-latitude weather and climate extremes (Petoukhov and Semenov 2010; Francis et al. 2009; Francis and Vavrus 2012; Yang and Christensen 2012; Overland and Wang 2010; Hopsch et al. 2012; Garcia-Serrano and Frankkignoul 2014; Liptak and Strong 2014; Koenigk et al. 2016). Most of these studies found that reduced autumn ice extent leads to an atmospheric winter circulation that resembles the negative phase of the North Atlantic Oscillation (NAO). Particularly, eastern Europe and central Asia might respond with cold winter temperature anomalies to the autumn sea ice decline.
However, controversy exists regarding amplitude and robustness of the signal (Screen 2014; Barnes 2013; Barnes and Screen 2015). The relatively short time series of reliable sea ice data, together with a climate in transition makes the interpretation of possible linkages between sea ice reduction and mid-latitude climate extremes difficult. Analysis of long observational based time series and model simulations showed that the relation between the NAO and Arctic climate variables, as surface temperature, sea ice and oceanic heat transports, is not robust over time (Goosse and Holland 2005).
Changes in Arctic climate variations could also affect lower latitudes via oceanic linkages. The export of freshwater out of the Arctic alters the deep water formation in the North Atlantic (Häkkinen 1999; Haak et al. 2003; Koenigk et al. 2006). Dickson et al. (1988) and Belkin et al. (1998) suggested that the so called “Great Salinity Anomalies” in the 70s and 80s were mainly caused by strong ice exports through Fram Strait. Such variations in the Arctic freshwater exports have also the potential to affect the variability of the Atlantic Meridional Overturning Circulation (AMOC).
The ongoing Arctic climate change is superimposed by large interannual to multi-decadal variations. This low-frequency variability makes it difficult to extract the Arctic warming and its impact on lower latitudes due to anthropogenic warming from the observations. Screen et al. (2014) showed that Arctic internal variability can mask the sign of the response to Arctic sea ice decline.
In this study, we analyze three 100-year long climate simulations, using different constant external forcing, with a successor of the CMIP5 version of the EC-Earth global climate model. We aim to investigate the extent to which the Arctic climate responds to changes in the external forcing, and if we can learn something about the robustness of the observed Arctic climate changes and the suggested linkages to lower latitudes.
The article is organized as follows: following this introduction, the model and the simulations are described. Section 3 presents the results and Sect. 4 provides a summary and conclusions from this study.
2 Model and simulations
2.1 Model description
The model used in this study is the version 3.0.1 of the global coupled climate model EC-Earth (Hazeleger et al. 2010, 2012; Sterl et al. 2012), which is the successor of version 2.3 used for CMIP5, and which was also used by Batté and Doblas-Reyes (2015) and Davini et al. (2015). Compared to version 2.3, EC-Earth3.0.1 includes updated versions of its atmospheric and oceanic model components, as well as a higher horizontal and vertical resolution in the atmosphere.
The atmospheric component of EC-Earth is the Integrated Forecast System (IFS) of the European Centre for Medium Range Weather Forecasts (ECMWF). Based on cycle 36r4 of IFS, it is used at a T255 resolution, using a reduced Gauss-grid. The model has 91 vertical levels, thereof 50 above 200 hPa. The model top is at 0.01 hPa.
The ocean component is the Nucleus for European Modelling of the Ocean (NEMO, Madec 2008). It uses a tri-polar grid with poles over northern North America, Siberia and Antarctica with a resolution of about 1 degree (the so-called ORCA1-configuration) and 46 vertical levels (compared to 42 levels in the CMIP5 model version). The upper model level is at about 3 m and 10 levels are in the upper 100 m. The ocean model is based on NEMO version 3.3.1 and includes the Louvain la Neuve sea-ice model version 3 (LIM3, Vancoppenolle et al. 2012), which is a dynamic-thermodynamic sea-ice model. EC-Earth3.0.1 uses LIM3 with only one sea ice category. The atmosphere and ocean/sea ice parts are coupled through the OASIS (Ocean, Atmosphere, Sea Ice, Soil) coupler (Valcke 2006) every three hours.
Three 100-year long simulations with EC-Earth were performed, forced with three different constant greenhouse gas concentrations corresponding to year 2000 (EXP2000), 2015 (EXP2015) and 2030 (EXP2030), respectively. Concentration levels for year 2000 are based on observations; for years 2015 and 2030, the respective levels from the RCP4.5 emission scenario have been used. The CO2 concentration used in EXP2000 is the observed value from year 2000 of 368.87 ppm; in EXP2015 and EXP2030, 399.97 ppm and 435.05 ppm are used, respectively. The CO2 increase between EXP2030 and EXP2015 is thus slightly larger than the increase between EXP2015 and EXP2000. However, because of the logarithmical increase of the radiative forcing with increased CO2 concentration, the increase in radiative forcing is nearly linear across our simulations. All three simulations were started from the same initial conditions obtained from the end of a 200-year long present day (using constant year 2000 forcing) control simulation. EXP2000 is a continuation of this present day control simulation using exactly the same model version and configurations.
Note: our 100-year time series are too short to analyze multi-decadal variations in the ocean in detail. However, they allow to estimate the mean Arctic climate change caused by changes in the greenhouse gas forcing, and they allow to investigate the possible linkages between the Arctic and lower latitudes, and how these linkages might change under different greenhouse gas forcings.
Variations of the sea ice edge affect the atmosphere locally and possibly remotely. In a transient climate, the sea ice edge moves to the north with superimposed variations, which makes it almost impossible to extract the atmospheric response to the sea ice change from the sea ice variability. Note: our simulations differ only in the greenhouse-gas forcing. All other external forcings are the same in all three simulations. Thus, in contrast to observations and transient model simulations, our work really focuses on the differences caused by changes in the greenhouse-gas forcing.
Mean values and standard deviations (cursive) for the years 21–100 of annual mean Arctic T2m, global mean SST, September and March Arctic ice extent and volume and annual mean max AMOC in EXP2000, EXP2015, EXP2030 and estimates for the recent past (1980–2013, 2004–2013 for RAPID-MOCCHA)
Mean (years 21–100);
Annual mean T2m Arctic (70 –90 N) in °C
Annual mean global mean SST in °C
18.51 (ERAint); 18.57 (HADISST)
NH ice extent September in 106 km2
NH ice extent March in 106 km2
NH ice volume September in 103 km3
NH ice volume March in 103 km3
Annual mean max AMOC in Sv
19.1 (RAPID-MOCCHA, 26°N)
As for the global mean SST, Arctic mean 2 m air temperature (T2m) in EXP2030 (Fig. 1a) agrees best to the 1980–2013 period in the reanalysis data. However, due to sparse observations in the Arctic, uncertainties in reanalyses data are considerable as well (Jakobson et al. 2012). Jakobson et al. (2012) compared different reanalysis data sets in the Arctic and concluded that ERA-interim performs best. However, there is a tendency for a warm bias of locally up to 2 K in ERA-interim below 400 m.
The warming in the Arctic (Table 1) is amplified compared to the global mean T2m values (0.21 K in EXP2015 and 0.58 K in EXP2030) by a factor of about three and reaches 1.78 K in EXP2030. This compares to an observed Arctic warming of about 2 K since 1980. As for global mean values, the warming is stronger between EXP2030 and EXP2015 compared to the difference between EXP2015 and EXP2000. This agrees well with findings from Gregory et al. (2002) and Mahlstein and Knutti (2012).
All three simulations exhibit large decadal variations in a number of variables (Fig. 1). However, the mean changes of the variables in Fig. 1 are all significant at the 95 % significance level. To calculate the significance, we used a two-sided student t test and we calculated decorrelation-times to estimate the number of degrees of freedom (von Storch and Zwiers 1999).
Many future climate projections showed the smallest warming south of Greenland (Stocker et al. 2013) but only very few models simulated a significant cooling as found in our study. Also transient future climate projections with EC-Earth2.3 showed reduced warming but no cooling in this area (Koenigk et al. 2013). Observational based data, however, show a significantly negative temperature trend in this area south of Greenland (Rahmstorf et al. 2015), widely debated as the “Atlantic cold blob”. Rahmstorf et al. (2015) related this cooling to a reduction in the AMOC, especially after 1970. The strong cooling in our model might partly be due to the constant forcing as opposed to the transient forcing in CMIP5. The AMOC (Fig. 1b) and the associated oceanic northward heat transports vary on multi-decadal time scales. Thus, the transient year 2030 climate is affected by ocean water masses that have been formed decades before, in a climate with still high AMOC activity. In our experiments, the use of a constant forcing might increase the cooling effect from changes in the northward oceanic heat fluxes compared to the increased greenhouse gas forcing in transient climate simulations. All three experiments show large variations and a tendency to a weakening of the AMOC in the first two decades, particularly in EXP2030. Thereafter, the AMOC increases again but its average stays about 3 Sv smaller in EXP2030 than in EXP2000; also in EXP2015 a significant reduction occurs (compare Table 1). This reduction in our quasi-equilibrium simulations is almost three times as large as the AMOC reduction between year 2030 and year 2000 in transient CMIP5 projections with EC-Earth, and as large as the change until the second half of the twenty-first century in the CMIP5 simulations (Brodeau and Koenigk 2015). Towards the end of our 100-year simulations, the differences between the three simulations decrease. While part of this reduction is due to relatively low AMOC values in the last 20 years of EXP2000, we cannot rule out that a partial recovery of the AMOC in EXP2015 and EXP2030 contributes as well. Results by Blackport and Kushner (2016) indicated a recovery of an initial AMOC-reduction to Arctic sea ice loss after a few 100 years. Our experiment setup differs substantially from the experiments done by Blackport and Kushner (2016) but our time series are too short to see if a similar AMOC-recovery would take place. A partial recovery of the AMOC would likely lead to a less pronounced cold temperature blob.
The sea surface salinity (SSS) is strongly reduced in the same area of the North Atlantic where the ocean surface is getting cooler (Fig. 2d–f). Again, this is likely the consequence of the reduced transport of warm and salty water masses into this region. Also in the Central Arctic, the surface gets significantly fresher, likely due to increased freshwater input from rivers and increased precipitation (Koenigk et al. 2007, 2013). The ocean circulation stores most of the additional freshwater in the Beaufort Gyre or transports it in the Transpolar Drift Stream towards Fram Strait. An increased freshwater storage in the Beaufort Gyre is in agreement with observations (Giles et al. 2012). The increase of the salinity along some coastlines is likely caused by enhanced mixing due to longer periods with open water. Particularly along the North American coast, the mixed layer depth is increased (not shown). Enhanced sea ice melt in the Arctic might locally affect the surface salinity as well.
A somewhat surprising increase of salinity occurs in the Labrador Sea. This is surprising since the CMIP5 future simulations with EC-Earth (Brodeau and Koenigk 2015) indicated strongly reduced deep water convection in the Labrador Sea. This prevents that the relatively fresh surface layer is mixed with the underlying saltier layers, which would lead to a further reduction of SSS and convective activity. Both EXP2015 and EXP2030 show an increase of up to 300 m of the mixed layer depth (MXLD) in the area in the Labrador Sea where SSS increases. However, Fig. 2g–i show that this increase of MXLD occurs east of the main convection area in the Labrador Sea. In the main convection area, MXLD is reduced as expected.
Correlations calculated from 11-year running mean values
Correlations, 11-year running means
DMV Labrador Sea → AMOC
DMV leads 4 years
DMV Labrador Sea → T2m Arctic
DMV leads 4 years
AMOC → BSO
AMOC → Sep Arctic ice extent
AMOC → T2m Arctic
Heat transport BSO → T2m Arctic
Heat transport BSO → Sep Arctic ice extent lag 0
Heat transport Bering Strait → Sep Arctic ice extent, lag 0
Similar to the Labrador Sea, the MXLD in the Greenland Sea and in the Irminger Sea are strongly reduced. The DMV for the Greenland-Iceland-Norwegian Seas (GIN-Sea, Fig. 4b, d, f) shows a strong reduction in amplitude, particularly in EXP2030, compared to EXP2000 and might contribute to a reduced AMOC as well. However, no clear relation between the variations of the DMV in the GIN-Sea and the AMOC could be found (Figs. 1b, 4) and correlations are not significant. This might be explained by the less direct effect of the GIN-Sea bottom waters on the AMOC compared to the deep water that is formed in the Labrador Sea. During the complicated travel across the overflows towards the North Atlantic, the GIN-Sea bottom waters are more mixed with other water masses, and contribute thus less distinctly to the AMOC than the deep water that is formed in the Labrador Sea.
Table 2 shows a significantly positive correlation between the AMOC and mean Arctic temperature. A possible explanation is that the AMOC affects the heat transport into the Arctic and thus consequently the Arctic sea ice and air temperature. As such, the reduction of the AMOC should have a dampening effect on the Arctic temperature in EXP2015 and EXP2030. By regressing the 11-year running mean values of the AMOC on the Arctic temperature in our three simulations, and taking the standard deviation of the AMOC into account, the reduction of the AMOC between EXP2030 and EXP2000 would lead to a reduction of the Arctic temperature increase by 0.6–1.1 K. However, this assumes that the processes that link the variability of the AMOC with Arctic T2m are the same as the processes that relate the mean changes in the AMOC with the changes of Arctic T2m.
3.2 Sea ice
The simulated ice volume loss is quite large (from 15 to 9.9 million km3) but also smaller than estimates for the real ice volume reduction. However, sea ice extent and volume in the Arctic (Fig. 6) show pronounced decadal-scale variations in our simulations. These variations could mask or enhance human-induced trends at interannual to decadal scales. This finding is in agreement with a recent study by Swart et al. (2015) analyzing the internal sea ice variability in CMIP5 models.
Not only the ice reduction but also the simulated T2m increase in the Arctic in EXP2030 (1.78 K) is smaller than in ERA-interim since 1980 (about 2 K). Still, it seems that Arctic sea ice in our model might respond less sensitively to global warming than in reality.
Sea ice extent in EXP2030 is reduced after year 60, which is consistent with the warm Arctic temperature after year 60. As discussed in Sect. 3.1, the ocean heat transports into the Arctic through both the Barents Sea Opening and Bering Strait are high after year 60. Increased ocean heat fluxes through these sections are likely to reduce the Arctic sea ice extent as shown by previous observational (Schlichtholz 2011, Woodgate et al. 2010) and modelling studies (Koenigk and Brodeau 2014). We find a strongly negative correlation, exceeding −0.8 in all three experiments (Table 2), between the low-pass filtered (11-year running means) heat transport through BSO and the Arctic sea ice extent in September. The relation between the heat transport through the Bering Strait and sea ice extent seems to be more variable, the correlations vary substantially between the three simulations (Table 2).
Similar to the ice extent, the amplitude of the annual cycle of ice volume seems to be slightly underestimated in our EC-Earth simulations. However, note that ice thickness observations are still extremely uncertain and that the reference data from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS, Zhang and Rothrock 2003) used here should not be considered as the truth. In contrast to sea ice extent and air temperature, sea ice volume (Fig. 6c, d) is already considerably reduced between EXP2015 and EXP2000, and no clear acceleration until 2030 can be seen. This might indicate that the sea ice first has to be thinned down to a critical thickness before the ice extent is substantially reduced. This is partly in contrast to results from Holland et al. (2006), who linked the rate of summer ice retreat mainly to the interplay of simulated natural variability and forced changes. In agreement to our study, Holland et al. (2006) found an important influence of the ocean heat transport into the Arctic on sea ice variability and decline.
In September, sea ice decreases mainly in the Barents Sea and in the Beaufort Sea along the North America coast in EXP2015. In EXP2030, we see a more general retreat of sea ice along all ice edges with largest reductions in the Barents and Kara Seas. Compared to the sea ice change in transient CMIP5 climate projections with EC-Earth 2.3 (Koenigk et al. 2013), the response is somewhat less focused on the Barents Sea region.
3.3 Changes in the atmosphere
3.3.1 Atmospheric circulation
In summer, the SLP response to enhanced greenhouse gas forcing in EXP2015 and EXP2030 is generally relatively weak (below 1 hPa almost everywhere), although quite large areas with significant changes of SLP can be seen: increased SLP occurs over the North Atlantic, parts of Europe and Asia, and decreased SLP occurs west of Greenland and over the North Pacific (Fig. 8e, g).
The second EOF, which explains 15 % of the variance in EXP2000 and about 20 % in EXP2015 and EXP2030, shows a large signal over the northern North Pacific in all three simulations. However, it differs distinctly and significantly over the North Atlantic-European area: while EXP2000 has poles south of Iceland and over southwestern Europe, EXP2015 and EXP2030 both show a dipole with a large signal over northeastern Europe/Barents Sea region and southwestern Europe. This Barents Sea-southwestern Europe pattern can be found in EOF4 of EXP2000 but its importance is obviously growing in EXP2015 and EXP2030. Such a pattern favors northerly and easterly wind anomalies and thus cold winter conditions over parts of Asia and Eastern Europe, and resembles the observed atmospheric pattern in the cold winter 2010/2011.
3.3.2 Air temperature
The winter 2 m air temperature (Fig. 8, right) is already in EXP2015 significantly increased in most mid and high latitude regions compared to EXP2000. A region with a significant reduction of T2m occurs over the North Atlantic, which is related to the cooling in ocean surface temperature (Fig. 2) and to the reduced QTLA into the atmosphere (Fig. 3). The amplitude and extension of this cooling area is somewhat smaller for T2m compared to SST. This can be explained by the limited size of the Atlantic cold blob and the advection of warmer air masses from the surrounding areas with warmer SST. No significant T2m change occurs in an area extending from eastern Europe towards central Asia in EXP2015. The largest warming takes place over the Barents Sea with up to 3 K, otherwise the warming stays below 1.5 K over the continents and does not exceed 0.5 K over the ocean. In EXP2030, the warming in winter is strongly intensified and reaches more than 2 K in the entire Arctic (up to 5 K in the Barents Sea and Hudson Bay) and 1–2 K over the mid and high-latitude continents. Over the North Atlantic, instead, the cooling further amplifies and reaches −1 to −2 K southeast of Greenland. The strong warming in the Barents Sea is a consequence of the retreat of sea ice and enhanced surface heat fluxes. In the Hudson Bay, the sea ice concentration does not change but the ocean heat loss to the atmosphere increases. This might be due to reduced ice thickness in winter (not shown). Furthermore, the SLP-changes (Fig. 8a, c) indicate anomalous southerly winds that advect warmer air masses into the Hudson Bay area.
In summer, the warming is more evenly distributed with about 0–1 K in EXP2015 and 0.5–1.5 K in EXP2030. Again, a significant cooling occurs over the North Atlantic south of Greenland.
In EXP2015 and EXP2030, the strongest warming signal occurs near the surface, which tends to reduce the winter inversion strength and thus the atmospheric stability in the Arctic. Compared to lower latitudes, the temperature amplification decreases with height and disappears above 800 hPa in EXP2015. In EXP2030, the amplification extends far more up (up to 500 hPa) and is largest near the pole, while in EXP2015 it is more constrained to 70–85°N.
In summer, the Arctic atmosphere is relatively uniformly warmed with 0–1 K in EXP2015 and up to 1.5 K in EXP2030 (not shown).
The zonal mean distribution of the specific humidity follows closely the temperature distribution in EXP2000 (Fig. 11d). The changes in EXP2015 and EXP2030 show a much stronger increase of specific humidity at the surface at all latitudes, but contrarily to temperature, no amplification at high latitudes. The reason for this is the exponentially growing capacity of warmer air to uptake water vapor.
3.4 Arctic-mid latitude linkages
A number of studies suggested a possible link between the observed sea ice reduction and the large scale atmospheric circulation and mid-latitude air temperature (Jaiser et al. 2013; Inoue et al. 2012; Petoukhov and Semenov 2010; Hopsch et al. 2012; Overland et al. 2011; Peings and Magnusdottir 2014; Rinke et al. 2013; Koenigk et al. 2016). Most of these studies used either the difference between the last decade (with little ice) and the previous two (with much ice), or they used detrended time series to assess the atmospheric response to sea ice variability and trend. One common problem for all these studies is the fact that observational time series are very short. Moreover, they are derived from a climate in transition, which means that both external and internal forcing have likely changed during the three decades of observations.
In EXP2015, the correlation between November ice and winter SLP is still weak, although some slightly larger areas with significant correlations are found over eastern Siberia and central Europe. The strongest negative correlations between ice and T2m occur in the Barents Sea and its surroundings. Furthermore, we see more wide-spread negative correlations extending from Florida across the North Atlantic towards southern Europe, and negative correlations over the northeastern North Pacific. In EXP2030, the SLP response is similar as in EXP2015 over eastern Asia, but in addition, a significantly negative correlation occurs over the subtropical North Atlantic and over the western North Pacific. Significantly negative temperature correlations extend now all the way from the Caribbean across the North Atlantic, following the North Atlantic Current into the Arctic and further into the North Pacific and eastern Asia. Over a small area of the North Atlantic that spreads from eastern Canada to the south of Greenland, a positive correlation is found.
We performed the same correlation analysis with detrended data (not shown). The results are generally similar to the results from the analysis using the raw data. However, significantly positive correlations between sea ice and T2m occur over the North Atlantic subpolar gyre (up to r = 0.32) and weaker negative correlations along the North Atlantic Current compared to the correlations of the raw data.
4 Summary and conclusions
Different states of Arctic climate have been analyzed in three quasi-equilibrium simulations with the global coupled model EC-Earth. Each of these simulations was forced by a constant greenhouse gas forcing, corresponding to years 2000, 2015 and 2030, with the latter two based on the RCP4.5 emission scenario. The Arctic temperature shows an amplified warming under 2030-level greenhouse gas forcing compared to the warming under year-2015 forcing. This goes along with an accelerated reduction of the Arctic sea ice extent between EXP2030 and EXP2015 compared to the reduction between EXP2015 and EXP2000. Both Arctic warming and sea ice reduction are closely linked to the increase of ocean heat flux into the Arctic, particularly through the Barents Sea Opening. In contrast to the ice extent, the Arctic ice volume is reduced more linearly. This indicates that an initial thinning of sea ice towards a critical value is needed before ice concentration can substantially be reduced. Given the fact that EC-Earth3.0.1 has a cold bias of roughly one degree Celsius in the Arctic and slightly overestimates the ice extent and volume in EXP2000, it is possible that the real-world has already reached such a critical ice thickness and is already in the state of accelerated sea ice extent reduction. Our model simulations might therefore be more representative for changes in the recent past than for upcoming future changes.
Decadal variations of Arctic sea ice extent and ice volume are large in all three simulations. These variations are dominated by the variability of the ocean heat transports into the Arctic through the Barents Sea Opening and the Bering Strait. The simulated variations of ice extent reach about two-thirds of the observed ice extent reductions during the last 30 years. This underlines the difficulty to extract the trend (caused by increased greenhouse gas forcing) from the observed sea ice reduction signal, and thus to make clear statements on the ability of global climate models to simulate the observed sea ice trend.
Despite a general warming of mid and high northern latitudes under present day and near future forcing compared to the recent past greenhouse gas forcing, a substantial cooling occurs in the subpolar gyre of the North Atlantic. This cooling agrees well with the recently debated and observed Atlantic cold blob. It is likely related to strong reductions in the AMOC and the associated weaker northward oceanic heat transports in EXP2030 and EXP2015 compared to EXP2000. In EXP2030, the AMOC is reduced by about 3 Sv, which is almost three times as large as the AMOC reduction between year 2030 and year 2000 in transient climate simulations with the CMIP5 model version of EC-Earth. The weakening of the AMOC in our simulations is mainly caused by reduced deep water formation in the Labrador Sea. Since the AMOC is highly positively correlated with the Arctic temperature in all three simulations, the reduction of the AMOC might have a dampening effect on the Arctic temperature increase in EXP2015 and EXP2030. AMOC differences between our simulations decrease towards the end of the simulations. It remains unclear if this is only due to variations or if the AMOC is partly recovering in EXP2015 and EXP2030. In the latter case, also the cold blob in the North Atlantic would likely become less pronounced.
After year 60, EXP2030 experiences a temperature increase in the Arctic. This is related to a resurgence of deep-water convection in the Labrador Sea—after a shutdown of 40 years—and an associated increase of the AMOC and the northward heat transport through the Barents Sea into the Arctic. As a consequence of this warming, Arctic sea ice extent, volume and export through the Fram Strait are decreased. It remains unclear if this warming period is a late response to the changes in greenhouse gas forcing or if it is caused by natural climate variations.
The vertical temperature change in winter in EXP2015 and EXP2030 compared to EXP2000 is dominated by a near surface amplification in high northern latitudes. While the warming is largest at about 78°N and near the surface in EXP2015, EXP2030 shows a stronger temperature amplification in all Arctic latitudes, which extends vertically up to 500 hPa height.
The simulated responses to 2015 and 2030-greenhouse gas forcing do not reproduce any of the much debated observed trend patterns, such as the trend towards a more negative NAO-index or the cooling trend over parts of Eastern Europe and Asia if using the last 80 years of our simulations. However, when comparing selected 30-year periods, both negative and positive NAO-like changes, which are of similar amplitude as the observed trends over the last 30 years, are found. This indicates that either our model is not fully able to reproduce the observed relationship between sea ice reduction and NAO, or the observed trends might not be robust over longer time periods. The observed trends might at least partly be caused by natural variations.
Furthermore, we do not find any clear impact of Arctic ice variations on remote regions in mid and high latitudes when considering the entire length of the simulations; we mainly find a local response in the area of the ice anomaly. This local response increases from recent past to near future. However, for shorter time periods, large variations in the response of the large-scale atmospheric circulation to sea ice variations occur. We find 30-year periods with both NAO+ and NAO-like responses to Arctic sea ice reductions. First, this shows that EC-Earth is generally able to reproduce the observed atmospheric response to sea ice variations. Second, it indicates the possibility that this relation might not be robust over time in the real world either. Internal climate variability is too large to allow final conclusions from 30 years of observations.
This study has been made possible by support of the Rossby Centre at the Swedish Meteorological and Hydrological Institute (SMHI) and the Bolin Centre for Climate Research together with the NORDFORSK Top Level research Initiative, Project No. 61841-GREENICE. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre at Linköping University (NSC).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.