Surface Air Temperatures
Figure 1 shows the ensemble mean seasonal temperature anomalies for the eight models we analyze. Arctic temperatures in G1 are about 2°C higher than piControl in winter and about 1°C higher in summer. This is a far smaller change than the abrupt4xCO2 increases which average about 10°C annually. However, local temperature changes are much greater than these means, with some regions being up to 6°C warmer than piControl under G1, particularly in autumn and winter, and a few places being noticeably cooler by up to 2°C, especially North Greenland in summer. Summer is, in general, the coolest season under G1 relative to piControl. Similar as with abrupt4xCO2 and consistently across the models, the maximum warming in G1 relative to piControl is found over the Barents Sea area in winter (Figure S1) and is related to the sea ice reduction that we will discuss next.
Figure 1. Multimodel ensemble mean seasonal surface air temperature anomalies (K) for the Arctic region for (left) abrupt4xCO2-piControl and (right) G1-piControl. Stippling shows regions where less than six of eight models agree on the sign of the response. Figure S1 shows the individual model results.
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Sea Ice Extent and Concentrations
Figure 2 shows the seasonal cycle of sea ice extent. The across-model scatter for each of the three experiments is greater at maximum extent in March than at minimum in September (scatter of ~ 6 and 3 million km2, respectively, for G1). While the ensemble average shows dramatic reductions in sea ice extent for the abrupt4xCO2 experiment compared with piControl (five of eight models show a summertime ice-free Arctic), the G1 experiment shows only a slight decrease in extent relative to piControl so that it maintains the preindustrial sea ice extent of ~ 9 million km2 in September and ~ 17 million km2 in March. The timings of the maximum and minimum sea ice extents remain in March and September, respectively, despite the large differences in forcing across the three experiments. Table 1 also shows that sea ice total area in the models decreases much more in September than in March, and this difference reflects a relative increase in first-year ice and decrease in multiyear ice in almost all models under both abrupt4xCO2 and G1. Even though the total ice area under G1 is almost the same as that of piControl (a decrease of 3% in the G1 ensemble mean annual ice area, relative to piControl), the loss of multiyear ice (−7%) suggests a thinner and more mobile ice cover in G1 than under piControl conditions (Table 1).
Figure 2. Multimodel ensemble mean monthly sea ice extent (million km2; following the standard definition of area of the ocean with sea ice concentration of at least 15%) for abrupt4xCO2 (red), piControl (black), and G1 (blue). The ensemble mean is the thick line, light blue and pink bands show the full range of variability of across-model mean monthly values for G1 and abrupt4xCO2, respectively, and error bars shows the piControl across-model range.
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Table 1. Relative Sea Ice Area Change for Multiyear and First-Year Sea Ice Following Zhang and Walsh 
|Relative Change From piControl (%)||Relative Change From piControl (%)|
|Annual||Multiyear (September)||First Year (March–September)||Annual||Multiyear (September)||First Year (March–September)|
Therefore, we may conclude that the G1 forcing achieves the target of maintaining preindustrial conditions for Arctic sea ice extent. However, this slight total area change may mask considerable regional changes in sea ice if reorganization of atmospheric and ocean circulation occurs under the G1 forcing. Since sea ice is challenging for models to simulate because of a variety of complex processes (e.g., ice and snow cover melt/growth, ice transport, rafting, ridging, and subgrid cell features), regional responses in sea ice concentration show pronounced across-model scatter. Accordingly, in Figure 3 we investigate maps of sea ice concentrations showing the results from all the models for the two sets of anomalies abrupt4xCO2-piControl and G1-piControl, and we illustrate the yearly behavior by showing the conditions in the annual minimum represented by the September maps and the annual maximum extent with the March maps. When reading the difference plots it should be noted that a difference of 20% in March could mean that sea ice concentrations are reduced from, for example, 80–60% (over the central Arctic Ocean), or it could mean that a 20% cover is reduced to zero (marginal seas). For reference, Figure S2 shows the actual sea ice concentration maps for piControl, G1, and abrupt4xCO2 for March and September. The seasonal maps of the differences abrupt4xCO2-piControl and G1-piControl for all models are shown in Figure S3. It has been noted previously that some models (including GISS-E2-R used here) have unrealistic sea ice thickness distribution, while others (including IPSL-CM5A-LR and NorESM1-M) overestimate the extent of sea ice in historical simulations [Maslowski et al., 2012]. Here we see that GISS-E2-R and IPSL-CM5A-LR overestimate March sea ice extent in piControl.
Figure 3. Model simulations of March (maximum sea ice extent) and September (minimum extent) sea ice concentration anomalies (%) for abrupt4xCO2-piControl and G1-piControl for the eight models we analyze here. The ensemble mean has stippling where less than six of eight models agree on sign of change. Figure S2 shows the actual sea ice concentration map for each experiment.
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It is immediately obvious that abrupt4xCO2 forcing reduces sea ice concentration by 90% in many models and regions during summer and autumn seasons. Five out of the eight models are practically ice free under abrupt4xCO2 in September (Figure 3). The largest March sea ice reductions common to models are in the Barents, Kara, and Greenland Seas. This can be related to largest warming in these seas (Figure 1).
In contrast, the G1-piControl anomalies show sea ice concentration changes of less than about ±20%. Thus, G1 returns the sea ice concentrations almost to preindustrial conditions and prevents the dramatic sea ice loss seen under abrupt4xCO2. There is a clear correlation between anomalies of sea ice concentration and surface temperatures (Figures 1, 3, S1, and S3 and Table S2). In most regions and all models (except GISS-E2-R), G1 simulates reduced sea ice concentrations but with increased sea ice concentration in some places. The spatial patterns of regional sea ice concentration decrease/increase differ between the individual models, which likely reflect changes in regional winds and ocean currents (see section 4 for detailed discussion).
In March, models behave differently in the Barents Sea (some show decreases, and some show increases) under G1. Our results show that the sign of the sea ice cover change in the Barents Sea under G1 is related to the ice simulation in that region in piControl: models which simulate small ice cover in piControl simulate increased ice cover there in G1, and models which simulate large ice extent in piControl simulate decreased ice there in G1.
Figure 3 further indicates that many models show a striking tongue of increased sea ice concentration in the Greenland Sea under G1 in March. This feature is coincident with a region of reduced surface air temperatures and a larger area of lesser temperature rise (Figure S1) and also partly reflects a change in the ice drift path along the east Greenland current and the westerly Jan Mayen current. The same two mechanisms (cooling and sea ice drift) are also seen under abrupt4xCO2 and explain the occurrence of local sea ice increase in Greenland Sea or north Labrador Sea in some models (see section 4 for more details).
In September, the Laptev, East Siberian, Chukchi, and Beaufort Seas appear to have relatively less ice cover, while in part more is present in the Barents, Greenland, and Nordic Seas regions. This is a robust feature across the eight models. These regional summer sea ice concentration changes under G1, compared to piControl, are related to the regional temperature changes as well as with regional changes in atmospheric wind and ice drift (see section 4 for more details).
Figure 4 shows that G1 also maintains the pattern of piControl sea ice cover interannual variability (i.e., the year-to-year changes of seasonal means calculated by the seasonal standard deviation), with high variability in March and September along the sea ice edges. Thus, in general, sea ice variability in the marginal ice zone under G1 is similar as in piControl and far different from abrupt4xCO2, which shows higher sea ice cover variability in September than in March, associated with the thin ice located in the central Arctic Ocean. The increase/decrease of sea ice concentration along the North Atlantic sea ice margins in G1 compared to piControl seen in the different models (Figure 3) is, to some extent, associated with decreased/increased variability in sea ice concentrations from year-to-year (Figure S4) and suggestive of variability in warm air advection (associated with cyclone activity) and water inflow (associated with the Norwegian Atlantic, East Greenland, and Jan Mayen currents), as discussed later in section 4.2.
Figure 4. Multimodel ensemble mean standard deviation of March and September sea ice concentration (%) for abrupt4xCO2, G1, and piControl and the anomalies for abrupt4xCO2-piControl and G1-piControl. Stippling shows where less than six of eight models agree on sign of change. Figure S4 shows the individual model results.
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The changes in sea ice concentration (section 3.2; Figures 3 and S3) are expected to force regional changes in atmospheric circulation, via changed surface temperature, turbulent heat fluxes, and baroclinicity, which affects synoptic cyclone activity which in turn interacts with the large-scale circulation (see the review by Budikova [2009, and references therein]). Maps of sea ice and temperature are correlated with each other observationally [Ogi et al., 2008] and in models (Tables S2 and S3). Here we examine the mean sea level pressure (SLP) describing the near-surface atmospheric dynamics and the atmospheric circulation at higher levels. For the latter we analyze the changes in the geopotential height at 500 hPa and 200 hPa and the upper tropospheric wind at 200 hPa (the height of the jets).
SLP anomalies (Figure 5) resemble the temperature pattern (Figure 1) with warming correlated with a decrease of SLP, particularly in winter and autumn. Under abrupt4xCO2, the strongest negative SLP anomalies (up to −5 hPa in the ensemble mean and up to −8 hPa in individual models; Figure S5) are very consistent among the models and located over the Barents/Kara Seas and Bering/Chukchi/East Siberian Seas regions in winter and over the Arctic Ocean in autumn. This is a manifestation of the strongest warming and reduction in sea ice under abrupt4xCO2. The G1 experiment basically maintains the seasonal SLP of piControl. The anomalies of G1-piControl are within ±1.5 hPa in the ensemble mean (within ±3 hPa in individual models; Figure S5), i.e., about half of magnitude of those seen in abrupt4xCO2-piControl. But in some regions the anomalies are comparable to each other, namely over the Kara/Laptev Seas area in spring and over the northern North Atlantic and Barents Sea in summer and autumn. This highlights the importance of circulation changes in G1, even though the surface air temperature anomalies are one tenth as large as the abrupt4xCO2 anomalies. In G1, most models agree on a reduced SLP over the Arctic and an increase over parts of the lower latitudes (with the largest increase in Bering Sea and northern North Atlantic) in winter and spring, compared with piControl, although there is a large scatter among the individual model regional response patterns (Figure S5). The consistent, across-model SLP reduction over the Barents/Kara Seas in winter in G1 (Figures 5 and S5) is associated with the regional sea ice reduction and warming, as in abrupt4xCO2 (Figures 1, 3, S1, and S3). In summer and autumn, the SLP anomalies G1-piControl show a general increase, with a consistent, across-model regional maximum over the northern North Atlantic region.
Figure 5. Multimodel ensemble mean seasonal sea level pressure anomalies (hPa) for (left) abrupt4xCO2-piControl and (right) G1-piControl. Stippling shows regions where less than six of eight models agree on the sign of the response. Figure S5 shows the individual model results.
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At higher levels in the atmosphere, the geopotential heights (at 500 hPa and 200 hPa) show a general significant increase under abrupt4xCO2 with a rather zonally symmetric structure in summer and a much stronger, zonally asymmetric component displaying a pronounced wave structure in winter (Figures 6 and 7). In both abrupt4xCO2 and G1 experiments the models with weaker warming (Figure S1) show weaker changes in atmospheric circulation patterns (Figures S6 and S7). The G1-piControl 500 hPa geopotential height anomalies are approximately one third of the magnitude of the abrupt4xCO2-piControl changes and within ±50 m; the G1 model ensemble mean anomalies are less than 20 m relative to piControl (Figures 6 and S6). Generally, the G1 experiment maintains the geopotential at all height levels at its piControl magnitude (Figures 6 and 7). The spatial patterns of geopotential height anomalies G1-piControl are rather consistent across the models, with a larger intermodel scatter in winter than in other seasons. This may be caused by intermodel differences in surface thermal forcing and/or interaction with the baroclinic waves that affect planetary waves most strongly in winter.
Figure 7. Multimodel ensemble mean seasonal 200 hPa wind speed anomalies (m/s; color) for (left) abrupt4xCO2-piControl and (right) G1-piControl. Stippling shows regions where less than six of eight models agree on the sign of the wind speed response. The black isolines show the 200 hPa geopotential height anomalies (m) with intervals of 25 m in Figure 7 (left; abrupt4xCO2-piControl) and intervals of 10 m in Figure 7 (right; G1-piControl). Figure S7 shows the individual model results.
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Interestingly, the winter 500 hPa geopotential height response in four models (BNU-ESM, EC-EARTH, IPSL-CM5A-LR, and MPI-ESM-LR; Figure S6) represents a Pacific North America (PNA)-like pattern [Wallace and Gutzler, 1981]—a wave train over the North Pacific/North American region with opposite centers of action near the Aleutian Low in the Pacific; near Florida, southeast USA; and near Alberta, northern Canada. The PNA phase is however opposite between the experiments. The negative PNA phase is established in G1-piControl, while the positive phase of PNA is seen in abrupt4xCO2-piControl. Earlier simulations of future climate found varying responses [Handorf and Dethloff, 2009; Hu et al., 2001], and Brandefelt and Körnich  found model-dependent responses which include positive and negative phases of PNA. They concluded that internal variability and intermodel differences play a role for the stationary wave response to the enhanced greenhouse gas forcing. Generally, changes in PNA are related to changes in Rossby waves and their propagation conditions [e.g., Hoskins and Karoly, 1981; Franzke et al., 2011] and changes in heat sources such as tropical sea surface temperatures [Hoskins and Karoly, 1981; Brandefelt, 2006]. Tropical Pacific atmospheric temperature anomalies (e.g., generated by an in-phase combination of El Niño–Southern Oscillation and Pacific Decadal Oscillation) result in transfer of energy from the tropics toward North America, which helps maintain a PNA-like anomaly [e.g., Yu and Zwiers, 2007]. Thus, our result indicates that the G1 experiment is able to excite stationary waves, even though much weaker than in abrupt4xCO2, which might affect global teleconnection patterns. However, our G1 results also indicate across-model scatter in the magnitude of the 500 hPa geopotential height responses (Figures 6 and S6). The discussed changes in geopotential height are accompanied with upper tropospheric wind changes (Figures 7 and S7). The negative PNA-like pattern in G1-piControl is associated with a westward shift of the jet stream toward East Asia, blocking activity over the high latitudes of the North Pacific Ocean, and a strong split-flow configuration over the central North Pacific Ocean. Figure 7 displays these features with a decreased jet speed over central, west Pacific region (with across-model differences, Figure S7), and the 200 hPa geopotential height map also shows a “blocking” high pressure over the northern North Pacific under G1. In contrast, under abrupt4xCO2, the East Asian jet stream is intensified and eastward expanded. Our results are consistent with previous climate change studies which described a strengthening, broadening, and northward shift of the tropospheric zonal jets [e.g., Lorenz and DeWeaver, 2007; Brandefelt and Körnich, 2008] and with the findings of Linkin and Nigam  that PNA is associated with strong fluctuations in the strength and location of the East Asian jet stream.