Your search

Study of the mass balance of the Antarctic Ice Sheet is critical to estimate its potential contribution to global sea-level rise in the future. As the largest drainage system, the Lambert Glacier–Amery Ice Shelf drainage system plays an important role in the mass balance of the Antarctic Ice Sheet. In this study, the ice thickness measured by airborne ice-penetrating radar with high spatial resolution and accuracy and accurate ice velocity measured by in situ GPS stations along the route of the Chinese National Antarctic Research Expedition inland traverse were used to calculate the ice flux with unprecedented accuracy. This transverse is from Zhongshan Station to Dome A, passing through the east side of the Lambert Glacier and the smaller coastal glacier in the C-Cp basin. The results show that the ice flux across the entire traverse is 24.7 ± 2.8 Gt a−1, along which the section in drainage basin B–C (Lambert Glacier) has an ice flux of 20.9 ± 1.9 Gt a−1 and the section in drainage basin C–Cp (basin adjacent to Lambert Glacier) contributed 3.8 ± 0.4 Gt a−1. The ice flux values in both regions are coincident with the mass balance calculated from the Ice, Cloud, and Land Elevation Satellite, Earth Observing System. Meanwhile, the C–Cp basin shows an ice flux value of 6.6 ± 0.8 Gt a−1 across the grounding line.

The boreal spring Antarctic Oscillation (AAO) has a significant impact on the spring and summer climate in China. This study evaluates the capability of the NCEP’s Climate Forecast System, version 2 (CFSv2), in predicting the boreal spring AAO for the period 1983–2015. The results indicate that CFSv2 has poor skill in predicting the spring AAO, failing to predict the zonally symmetric spatial pattern of the AAO, with an insignificant correlation of 0.02 between the predicted and observed AAO Index (AAOI). Considering the interannual increment approach can amplify the prediction signals, we firstly establish a dynamical–statistical model to improve the interannual increment of the AAOI (DY AAOI), with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice. This dynamical–statistical model demonstrates good capability in predicting DY AAOI, with a significant correlation coefficient of 0.58 between the observation and prediction during 1983–2015 in the two-year-out cross-validation. Then, we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI. The improved AAOI shows a significant correlation coefficient of 0.45 with the observed AAOI during 1983–2015. Moreover, the unrealistic atmospheric response to March–April–May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO. This study gives new clues regarding AAO prediction and short-term climate prediction.

The latent heat fluxes (LHF) and sensible heat fluxes (SHF) over the Southern Ocean from six different data sets are inter-compared for the period 1988- 2000. The six data sets include three satellite-based products, namely, the second version of the Goddard Satellite-Based Surface Turbulent Fluxes data set (GSSTF-2), the third version of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS-3) and the Japanese Ocean Fluxes Data Sets with Use of Remote Sensing Observations (J-OFURO); two global reanalysis products, namely, the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 data set (NCEP-2) and the European Centre for Medium-Range Weather Forecasts 40 Year Re-analysis data set (ERA-40); and the Objectively Analyzed Air-Sea Fluxes for the Global Oceans data set (OAFlux). All these products reveal a similar pattern in the averaged flux fields. The zonal mean LHF fields all exhibit a continuous increase equatorward. With an exception of HOAPS-3, the zonal mean SHF fields display a minimum value near 50°S, increasing both pole- and equatorward. The differences in the standard deviation for LHF are larger among the six data products than the differences for SHF. Over the regions where the surface fluxes are significantly influenced by the Antarctic Oscillation and the Pacific-South American teleconnection, the values and distributions of both LHF and SHF are consistent among the six products. It was found that the spatial patterns of the standard deviations and trends of LHF and SHF can be explained primarily by sea-air specific humidity and temperature differences; wind speed plays a minor role. Keywords: Latent heat flux; sensible heat flux; Southern Ocean.

This study investigates systematically the intraseasonal variability of surface air temperature over Antarctica by applying empirical orthogonal function (EOF) analysis to the National Centers for Environmental Prediction, US Department of Energy, Reanalysis 2 data set for the period of 1979 through 2007. The results reveal the existence of two major intraseasonal oscillations of surface temperature with periods of 26 - 30 days and 14 days during the Antarctic winter season in the region south of 60°S. The first EOF mode shows a nearly uniform spatial pattern in Antarctica and the Southern Ocean associated with the Antarctic Oscillation. The mode-1 intraseasonal variability of the surface temperature leads that of upper atmosphere by one day with the largest correlation at 300-hPa level geopotential heights. The intraseasonal variability of the mode-1 EOF is closely related to the variations of surface net longwave radiation the total cloud cover over Antarctica. The other major EOF modes reveal the existence of eastward propagating phases over the Southern Ocean and marginal region in Antarctica. The leading two propagating modes respond to Pacific-South American modes. Meridional winds induced by the wave train from the tropics have a direct influence on the surface air temperature over the Southern Ocean and the marginal region of the Antarctic continent. Keywords: Antarctic climate, surface air temperature, intraseasonal variability, Antarctic Oscillation.

The Antarctic ice sheet has been losing mass over past decades through the accelerated flow of its glaciers, conditioned by ocean temperature and bed topography. Glaciers retreating along retrograde slopes (that is, the bed elevation drops in the inland direction) are potentially unstable, while subglacial ridges slow down the glacial retreat. Despite major advances in the mapping of subglacial bed topography, significant sectors of Antarctica remain poorly resolved and critical spatial details are missing. Here we present a novel, high-resolution and physically based description of Antarctic bed topography using mass conservation. Our results reveal previously unknown basal features with major implications for glacier response to climate change. For example, glaciers flowing across the Transantarctic Mountains are protected by broad, stabilizing ridges. Conversely, in the marine basin of Wilkes Land, East Antarctica, we find retrograde slopes along Ninnis and Denman glaciers, with stabilizing slopes beneath Moscow University, Totten and Lambert glacier system, despite corrections in bed elevation of up to 1 km for the latter. This transformative description of bed topography redefines the high- and lower-risk sectors for rapid sea level rise from Antarctica; it will also significantly impact model projections of sea level rise from Antarctica in the coming centuries.

We present Bedmap2, a new suite of gridded products describing surface elevation, ice-thickness and the seafloor and subglacial bed elevation of the Antarctic south of 60° S. We derived these products using data from a variety of sources, including many substantial surveys completed since the original Bedmap compilation (Bedmap1) in 2001. In particular, the Bedmap2 ice thickness grid is made from 25 million measurements, over two orders of magnitude more than were used in Bedmap1. In most parts of Antarctica the subglacial landscape is visible in much greater detail than was previously available and the improved data-coverage has in many areas revealed the full scale of mountain ranges, valleys, basins and troughs, only fragments of which were previously indicated in local surveys. The derived statistics for Bedmap2 show that the volume of ice contained in the Antarctic ice sheet (27 million km3) and its potential contribution to sea-level rise (58 m) are similar to those of Bedmap1, but the mean thickness of the ice sheet is 4.6% greater, the mean depth of the bed beneath the grounded ice sheet is 72 m lower and the area of ice sheet grounded on bed below sea level is increased by 10%. The Bedmap2 compilation highlights several areas beneath the ice sheet where the bed elevation is substantially lower than the deepest bed indicated by Bedmap1. These products, along with grids of data coverage and uncertainty, provide new opportunities for detailed modelling of the past and future evolution of the Antarctic ice sheets.