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We investigate the relationship between atmospheric circulation variability and the recent trends in Antarctic sea ice extent (SIE) using Coupled Model Intercomparison Project Phase 5 (CMIP5) atmospheric data, ECMWF Interim reanalysis fields and passive microwave satellite data processed with the Bootstrap version 2 algorithm. Over 1979–2013 the annual mean total Antarctic SIE increased at a rate of 195 × 103 km2 dec−1 (1.6 % dec−1), p < 0.01. The largest regional positive trend of annual mean SIE of 119 × 103 km2 dec−1 (4.0 % dec−1) has been in the Ross Sea sector. Off West Antarctica there is a high correlation between trends in SIE and trends in the near-surface winds. The Ross Sea SIE seasonal trends are positive throughout the year, but largest in spring. The stronger meridional flow over the Ross Sea has been driven by a deepening of the Amundsen Sea Low (ASL). Pre-industrial control and historical simulations from CMIP5 indicate that the observed deepening of the ASL and stronger southerly flow over the Ross Sea are within the bounds of modeled intrinsic variability. The spring trend would need to continue for another 11 years for it to fall outside the 2 standard deviation range seen in 90 % of the simulations.

Ice cores provide a wealth of information about past climate and atmospheric circulation however a good understanding of the precipitation patterns, potential source regions and transport pathways is essential in their interpretation. Here we investigate the precipitation pathways for a transect of five new ice cores drilled in the southern Antarctic Peninsula and Ellsworth Land. We utilize in situ observations from automatic weather stations to confirm that the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis data adequately captures annual and sub-annual variability, with evidence of a slight cold bias in the 2 m temperatures. Back trajectory analysis, from the British Atmospheric Data Centre trajectory service, reveals that warm and snowy years are associated with air masses that originate (5 days before reaching the site) from the Amundsen-Bellingshausen Sea, while cold and dry years are associated with air masses from the Antarctic continent. There is a clear seasonal migration in the trajectories at each site, reflecting the east to west migration of the Amundsen Sea Low, known to have a strong influence on climate in this region.

The influence of changes in winds over the Amundsen Sea has been shown to be a potentially key mechanism in explaining rapid loss of ice from major glaciers in West Antarctica, which is having a significant impact on global sea level. Here, Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model data are used to assess twenty-first century projections in westerly winds over the Amundsen Sea (U AS ). The importance of model uncertainty and internal climate variability in RCP4.5 and RCP8.5 scenario projections are quantified and potential sources of model uncertainty are considered. For the decade 2090–2099 the CMIP5 models show an ensemble mean twenty-first century response in annual mean U AS of 0.3 and 0.7 m s−1 following the RCP4.5 and RCP8.5 scenarios respectively. However, as a consequence of large internal climate variability over the Amundsen Sea, it takes until around 2030 (2065) for the RCP8.5 response to exceed one (two) standard deviation(s) of decadal internal variability. In all scenarios and seasons the model uncertainty is large. However the present-day climatological zonal wind bias over the whole South Pacific, which is important for tropical teleconnections, is strongly related to inter-model differences in projected change in U AS (more skilful models show larger U AS increases). This relationship is significant in winter (r = −0.56) and spring (r = −0.65), when the influence of the tropics on the Amundsen Sea region is known to be important. Horizontal grid spacing and present day sea ice extent are not significant sources of inter-model spread.

This study presents projections of twenty-first century wintertime surface temperature changes over the high-latitude regions based on the third Coupled Model Inter-comparison Project (CMIP3) multi-model ensemble. The state-dependence of the climate change response on the present day mean state is captured using a simple yet robust ensemble linear regression model. The ensemble regression approach gives different and more precise estimated mean responses compared to the ensemble mean approach. Over the Arctic in January, ensemble regression gives less warming than the ensemble mean along the boundary between sea ice and open ocean (sea ice edge). Most notably, the results show 3 °C less warming over the Barents Sea (~7 °C compared to ~10 °C). In addition, the ensemble regression method gives projections that are 30 % more precise over the Sea of Okhostk, Bering Sea and Labrador Sea. For the Antarctic in winter (July) the ensemble regression method gives 2 °C more warming over the Southern Ocean close to the Greenwich Meridian (~7 °C compared to ~5 °C). Projection uncertainty was almost half that of the ensemble mean uncertainty over the Southern Ocean between 30° W to 90° E and 30 % less over the northern Antarctic Peninsula. The ensemble regression model avoids the need for explicit ad hoc weighting of models and exploits the whole ensemble to objectively identify overly influential outlier models. Bootstrap resampling shows that maximum precision over the Southern Ocean can be obtained with ensembles having as few as only six climate models.

The semi-annual oscillation (SAO) in sea-level pressure at high southern latitudes is the consequence of a twice-yearly contraction (and strengthening) and expansion (and weakening) of the storm track between 50 and 65°S, with the contracted phases in spring and autumn. In this study the extent to which inter-annual variability of the SAO is correlated with inter-annual variability in mid- to lower-stratospheric circulation at 60°S was determined using NCEP/NCAR Reanalysis 1 data for the period 1979–2009. The second harmonic of the annual cycle of an SAO index was used to assess SAO amplitude and phase (the date of the first peak of the second harmonic). Zonal mean zonal wind at 60°S was used as an index for atmospheric circulation. The results show that year-to-year variability in the SAO amplitude is significantly correlated with mid-stratospheric (10 hPa) circulation variability in late summer/early autumn (February–March) and late winter/early spring (August–September). However, variability in the SAO phase is significantly correlated with mid-stratospheric circulation variability in spring (September–November). These maxima in significant correlations at 10 hPa propagate down to the surface in approximately one month. The characteristics of upward planetary wave propagation alone do not explain the late summer/early autumn and spring maxima in correlations. Evidence is shown that internal reflection of stationary wave-number 1 is important for explaining the strong correlations in late summer/early autumn, but that large variability during polar vortex break-up dominates the spring correlations. The results may be important for understanding seasonal differences in how stratospheric ozone depletion influences tropospheric circulation.

The spatial and temporal distributions of marine cold air outbreaks (MCAOs) over the northern North Atlantic have been investigated using re-analysis data for the period from 1958 to 2007. MCAOs are large-scale outbreaks of cold air over a relatively warm ocean surface. Such conditions are known to increase the severity of particular types of hazardous mesoscale weather phenomena. We used a simple index for identifying MCAOs: the vertical potential temperature gradient between the sea surface and 700 hPa. It was found that atmospheric temperature variability is considerably more important than the sea surface temperature variability in governing both the seasonal and the inter-annual variability of MCAOs. Furthermore, a composite analysis revealed that a few well-defined and robust synoptic patterns are evident during MCAOs in winter. Over the Labrador and Irminger Seas the MCAO index was found to have a correlation of 0.70 with the North Atlantic Oscillation index, while over the Barents Sea a negative correlation of 0.42 was found.

For many locations around the globe some of the most severe weather is associated with outbreaks of cold air over relatively warm oceans, referred to here as marine cold-air outbreaks (MCAOs). Drawing on empirical evidence, an MCAO indicator is defined here as the difference between the skin potential temperature, which over open ocean is the sea surface potential temperature, and the potential temperature at 700 hPa. Rare MCAOs are defined as the 95th percentile of this indicator. Climate model data that have been provided as part of the Intergovernmental Panel on Climate Change (IPCC) Assessment Report Four (AR4) were used to assess the models' projections for the twenty-first century and their ability to represent the observed climatology of MCAOs. The ensemble average of the models broadly captures the observed spatial distribution of the strength of MCAOs. However, there are some significant differences between the models and observations, which are mainly associated with simulated biases of the underlying sea ice, such as excessive sea-ice extent over the Barents Sea in most of the models. The future changes of the strength of MCAOs vary significantly across the Northern Hemisphere. The largest projected weakening of MCAOs is over the Labrador Sea. Over the Nordic seas the main region of strong MCAOs will move north and weaken slightly as it moves away from the warm tongue of the Gulf Stream in the Norwegian Sea. Over the Sea of Japan there is projected to be only a small weakening of MCAOs. The implications of the results for mesoscale weather systems that are associated with MCAOs, namely polar lows and arctic fronts, are discussed.