Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations

The indirect effect of winter Arctic Oscillation (AO) events on the proceeding summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer time scales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in northern-hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the Adjusted Rand Index – a method for comparing spatial patterns of variability, and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability of the AO relatively well, although over-estimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean, and under-estimate the variability over the north Africa and southern Europe. They also under-estimate the importance of regions such as the Beaufort, East Siberian and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.

Seasonal Changes in Arctic Cooling After Single Mega Volcanic Eruption

To investigate the pure long-term influence of single mega volcanic eruption (SMVE) of universal significance on Arctic temperature changes in summer and winter, the Samalas eruption in Indonesia which is the largest eruption over the past millennium is selected as an ideal eruption for simulation study based on Community Earth System Model. The significant Arctic cooling lasts for 16 years after the Samalas eruption. The obvious Arctic cooling shifts from summer to winter, and this seasonal change of cooling after the SMVE only exists in the high-latitude Arctic region. The cooling range in Arctic summer is larger than that in winter during the first 2 years, due to the strong weakening effect of volcanic aerosol on summer incident solar radiation and the snow-ice positive feedback caused by the rapid expansion of summer sea ice, while the winter sea ice in the same period doesn’t increase obviously. Starting from the third year, the Arctic winter cooling is more intense and lasting than summer cooling. The direct weakening effect of aerosol on solar radiation, which is the main heat source in Arctic summer, is greatly weakened during this period, making summer cooling difficult to sustain. However, as the main heat source in Arctic winter, the sea surface upward longwave radiation, sensible heat, and latent heat transport still maintain a large decrease. Furthermore, sea ice expansion and albedo increase result in the decrease in solar radiation and heat absorbed and stored by the ocean in summer. And the isolation effect of sea ice expansion on air-sea heat transfer in winter during this period makes the heat transfer from the ocean to the atmosphere correspondingly reduce in winter, thus intensifying the Arctic winter cooling. Additionally, the Arctic Oscillation (AO) changes from the negative phase to the positive phase in summer after the SMVE (such as Samalas), while it is reversed in winter. This phase change of AO is also one of the reasons for the seasonal changes in Arctic cooling.

Characterizing the hygroscopicty of growing particles in the Canadian Arctic summer

The impact of aerosols on clouds is a well-studied, although still poorly constrained, part of the atmospheric system. New particle formation (NPF) is thought to contribute 40–80 % of the global cloud droplet number concentration, although it is extremely difficult to observe an air mass from NPF to cloud formation. NPF and growth occurs frequently in the Canadian Arctic summer atmosphere, although only a few studies have characterized the source and properties of these aerosols. This study presents cloud condensation nuclei (CCN) concentrations measured on board the CCGS Amundsen in the eastern Canadian Arctic Archipelago from 23 July to 23 August 2016 as part of the Network on Climate and Aerosols: Addressing Uncertainties in Remote Canadian Environments (NETCARE). The study was dominated by frequent ultrafine particle and/or growth events, and particles smaller than 100 nm dominated the size distribution for 92 % of the study period. Using κ-Kohler theory and aerosol size distributions, the mean hygroscopicity parameter (κ) calculated for the entire study was 0.12 (0.06–0.12, 25th–75th percentile), suggesting that the condensable vapours that led to particle growth were primarily non-hygroscopic, which we infer to be organic. Based on past measurement and modelling studies from NETCARE and the Canadian Arctic, it seems likely that the source of these non-hygroscopic, organic, vapours is the ocean. Examining specific growth events suggests that the mode diameter (Dmax) had to exceed 40 nm before CCN concentrations at 0.99 % SS started to increase, although a statistical analysis shows that CCN concentrations increased 13–274 cm−3 during all ultrafine particle and/or growth times (total particle concentrations > 500 cm−3, Dmax < 100 nm) compared to Background times (total concentrations < 500 cm−3) at SS of 0.26–0.99 %. This value increased to 25–425 cm−3 if the growth times were limited to times when Dmax was also larger than 40 nm. These results support past results from NETCARE by showing that the frequently observed ultrafine particle and growth events are dominated by a highly non-hygroscopic fraction, which we interpret to be organic vapours originating from the ocean, and that these growing particles can increase the background CCN concentrations at SS as low as 0.26 %, thus pointing to their potential contribution to cloud properties and thus climate through the radiation balance.

Spatial-Temporal Characteristics of Arctic Summer Climate Comfort Level in the Context of Regional Tourism Resources from 1979 to 2019

In the context of global warming, a key scientific question for the sustainable development of the Arctic tourism industry is whether the region’s climate is becoming more suitable for tourism. Based on the ERA5-HEAT (Human thErmAl comforT) dataset from the European Center for Medium-range Weather Forecasts (ECMWF), this study used statistical methods such as climatic tendency rate and RAPS to analyze the spatial-temporal changes in Arctic summer climate comfort zones from 1979 to 2019 and to explore the influence of changes in climate comfort on Arctic tourism. The results showed the following: (1) With the increase in the Arctic summer temperature, the universal thermal climate index (UTCI) rose significantly from 1979 to 2019 at a rate of 0.457 °C/10a. There was an abrupt change in 2001, when the climate comfort changed from “colder” to “cool”, and the climate comfort has remained cool over the past decade (2010–2019). (2) With the increase in Arctic summer temperatures, the area assessed as “comfortable” increased significantly from 1979 to 2019 at a rate of 2.114 × 105 km2/10a. Compared with the comfortable area in the 1980s, the comfortable area increased by 6.353 × 105 km2 over the past 10 years and expanded to high-latitude and high-altitude areas, mainly in Kola Peninsula, Putorana Plateau, and Verkhoyansk Mountains in Russia, as well as the Brooks Mountains in Alaska. (3) With the increase in Arctic summer temperatures, the number of days rated comfortable on 30% of the grid increased significantly from 1979 to 2019 (maximum increase: 31 days). The spatial range of the area with a low level of comfortable days narrowed and the spatial range of the area with a high level of such days expanded. The area with 60–70 comfortable days increased the most (4.57 × 105 km2). The results of this study suggest that global warming exerts a significant influence on the Arctic summer climate comfort level and provides favorable conditions for further development of regional tourism resources.

Welfare State Romance

Forster’s works responded to the heated reformist debates surrounding the passing of the 1911 Insurance Act, and they engaged with the question whether a new social ethos of responsibility and care would need to precede, or whether it would flow from, institutional reform. What Howards End calls ‘preparedness’—i.e. the attempt to protect the most vulnerable members of society against the risk of unemployment—is central to the generic instabilities of Forster’s novel. These instabilities are further heightened in Forster’s novel fragment Arctic Summer, which fails in championing the excitements of ‘romance’ over the perceived boredom of a life guarded against risk. We need to read with rather than against the grain of these texts by taking seriously both their progressive aspirations and their reparative attention to subject positions that are excluded from the period’s projects of reform.

Interannual and decadal variability of Arctic summer sea ice associated with atmospheric teleconnection patterns during 1850-2017

The interannual and decadal variability of summer Arctic sea ice is analyzed, using the longest reconstruction (1850-2017) of Arctic sea ice extent available, and its relationship with the dominant internal variabilities of the climate system is further investigated quantitatively. The leading empirical orthogonal function (EOF) mode of summer Arctic sea ice variability captures an in-phase fluctuation over the Arctic Basin. The second mode characterizes a sea ice dipolar pattern with out-of-phase variability between the Pacific Arctic and the Atlantic Arctic. Summer sea ice variability is impacted by the major internal climate patterns: the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO) and Dipole Anomaly (DA), with descending order of importance based on the multiple regression analyses. The internal climate variability of the five teleconnection patterns accounts for up to 46% of the total variance in sea ice mode 1 (thermodynamical effect), and up to 30% of the total variance in mode 2 (dynamical effect). Furthermore, the variability of sea ice mode 1 decreased from 46% during 1953-2017 to 28% during 1979-2017, while the variability of mode 2 increased from 11% during 1953-2017 to 30% during 1979-2017. The increasingly greater reduction of Arctic summer sea ice during the recent four decades was enhanced with the positive ice/ocean albedo feedback loop being accelerated by the Arctic amplification, contributed in part by the atmospheric thermodynamical forcing from -AO, +NAO, +DA, +AMO, and –PDO and by the dynamical transpolar sea ice advection and outflow driven by +DA- and +AMO-derived strong anomalous meridional winds. Further analysis, using multiple large ensembles of climate simulations and single-forcing ensembles, indicates that the mode 1 of summer sea ice, dominated by the multidecadal oscillation, is partially a forced response to anthropogenic warming.

Dynamics of gaseous oxidized mercury at Villum Research Station during the High Arctic summer

While much research has been devoted to the subject of gaseous elemental mercury (GEM) and gaseous oxidized mercury (GOM) in the Arctic spring during atmospheric mercury depletion events, few studies have examined the behavior of GOM in the High Arctic summer. GOM, once deposited and incorporated into the ecosystem, can pose a threat to human and wildlife health, though there remain large uncertainties regarding the transformation, deposition, and assimilation of mercury into the food web. Therefore, to further our understanding of the dynamics of GOM in the High Arctic during the late summer, we performed measurements of GEM and GOM, along with meteorological parameters and atmospheric constituents, and utilized modeled air mass history during two summer campaigns in 2019 and 2020 at Villum Research Station (Villum) in northeastern Greenland. Seven events of enhanced GOM concentrations were identified and investigated in greater detail. In general, the common factors associated with event periods at ground level were higher levels of radiation and lower H2O mixing ratios, accumulated precipitation, and relative humidity (RH), although none were connected with cold temperatures. Non-event periods at ground level each displayed a different pattern in one or more parameters when compared to event periods. Generally, air masses during event periods for both campaigns were colder and drier, arrived from higher altitudes, and spent more time above the mixed layer and less time in a cloud compared to non-events, although some events deviated from this general pattern. Non-event air masses displayed a different pattern in one or more parameters when compared to event periods, although they were generally warmer and wetter and arrived from lower altitudes with little radiation. Coarse-mode aerosols were hypothesized to provide the heterogenous surface for halogen propagation during some of the events, while for others the source is unknown. While these general patterns were observed for event and non-event periods, analysis of individual events showed more specific origins. Five of the seven events were associated with air masses that experienced similar conditions: transported from the cold, dry, and sunlit free troposphere. However, two events experienced contrasting conditions, with air masses being warm and wet with surface layer contact under little radiation. Two episodes of extremely high levels of NCoarse and BC, which appear to originate from flaring emissions in Russia, did not contribute to enhanced GOM levels. This work aims to provide a better understanding of the dynamics of GOM during the High Arctic summer.

Realism of simulated internal variability in Arctic sea ice

&lt;p&gt;Arctic summer sea ice has decreased dramatically over the last few decades, particularly in the summer months. The observed decline is faster than most CMIP5 models, but if internal variability is considered, models and observations are not inconsistent. With only one realization of reality in observations, it is difficult to disentangle the role of internal variability from the forced response. We directly compare one metric of internal variability by resampling both observations and models. So far we have compared five CMIP5 models from the CLIVAR multi-model large ensemble archive (CanESM2, CESM1, CSIRO MK36, GFDL ESM2M, and MPI ESM1). For the pan-Arctic, these models were found to have higher internal variability than observed by approximately 10-50% across models and seasons. Spatially, we find the variability in ice edge region is consistently modelled well in March. In September, although the member mean of the models shows both smaller absolute declines and smaller variation of such declines with resampling, the models have at least one member consistent with observations. This allows us to conclude that the models&amp;#8217; representation of this specific metric of internal variability is consistent with observations.&lt;/p&gt;