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Snowmelt processes on Antarctic sea ice are examined. We present a simple snowmelt indicator based on diurnal brightness temperature variations from microwave satellite data. The method is validated through extensive field data from the western Weddell Sea and lends itself to the investigation of interannual and spatial variations of the typical snowmelt on Antarctic sea ice. We use in-situ measurements of physical snow properties to show that despite the absence of strong melting, the summer period is distinct from all other seasons with enhanced diurnal variations of snow wetness. A microwave emission model reveals that repeated thawing and refreezing cause the typical microwave emissivity signatures that are found on perennial Antarctic sea ice during summer. The proposed melt indicator accounts for the characteristic phenomenological stages of snowmelt in the Southern Ocean and detects the onset of diurnal snow wetting. An algorithm is presented to map large-scale snowmelt onset based on satellite data from the period between 1988 and 2006. The results indicate strong meridional gradients of snowmelt onset with the Weddell, Amundsen, and Ross Seas showing earliest (beginning of October) and most frequent snowmelt. Moreover, a distinct interannual variability of melt onset dates and large areas of first-year ice where no diurnal freeze thawing occurs at the surface are determined.

The East Antarctic Ice Sheet is the largest, highest, coldest, driest, and windiest ice sheet on Earth. Understanding of the surface mass balance (SMB) of Antarctica is necessary to determine the present state of the ice sheet, to make predictions of its potential contribution to sea level rise, and to determine its past history for paleoclimatic reconstructions. However, SMB values are poorly known because of logistic constraints in extreme polar environments, and they represent one of the biggest challenges of Antarctic science. Snow accumulation is the most important parameter for the SMB of ice sheets. SMB varies on a number of scales, from small-scale features (sastrugi) to ice-sheet-scale SMB patterns determined mainly by temperature, elevation, distance from the coast, and wind-driven processes. In situ measurements of SMB are performed at single points by stakes, ultrasonic sounders, snow pits, and firn and ice cores and laterally by continuous measurements using ground-penetrating radar. SMB for large regions can only be achieved practically by using remote sensing and/or numerical climate modeling. However, these techniques rely on ground truthing to improve the resolution and accuracy. The separation of spatial and temporal variations of SMB in transient regimes is necessary for accurate interpretation of ice core records. In this review we provide an overview of the various measurement techniques, related difficulties, and limitations of data interpretation; describe spatial characteristics of East Antarctic SMB and issues related to the spatial and temporal representativity of measurements; and provide recommendations on how to perform in situ measurements.

An evaluation is made of ozone profiles retrieved from measurements of the nadir-viewing Global Ozone Monitoring Experiment (GOME) instrument. Currently, four different approaches are used to retrieve ozone profile information from GOME measurements, which differ in the use of external information and a priori constraints. In total nine different algorithms will be evaluated exploiting the optimal estimation (Royal Netherlands Meteorological Institute, Rutherford Appleton Laboratory, University of Bremen, National Oceanic and Atmospheric Administration, Smithsonian Astrophysical Observatory), Phillips-Tikhonov regularization (Space Research Organization Netherlands), neural network (Center for Solar Energy and Hydrogen Research, Tor Vergata University), and data assimilation (German Aerospace Center) approaches. Analysis tools are used to interpret data sets that provide averaging kernels. In the interpretation of these data, the focus is on the vertical resolution, the indicative altitude of the retrieved value, and the fraction of a priori information. The evaluation is completed with a comparison of the results to lidar data from the Network for Detection of Stratospheric Change stations in Andoya (Norway), Observatoire Haute Provence (France), Mauna Loa (Hawaii), Lauder (New Zealand), and Dumont d'Urville (Antarctic) for the years 1997–1999. In total, the comparison involves nearly 1000 ozone profiles and allows the analysis of GOME data measured in different global regions and hence observational circumstances. The main conclusion of this paper is that unambiguous information on the ozone profile can at best be retrieved in the altitude range 15–48 km with a vertical resolution of 10 to 15 km, precision of 5–10%, and a bias up to 5% or 20% depending on the success of recalibration of the input spectra. The sensitivity of retrievals to ozone at lower altitudes varies from scheme to scheme and includes significant influence from a priori assumptions.

In March 2002 the European Space Agency (ESA) launched the polar-orbiting environmental satellite Envisat. One of its nine instruments is the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, which is a medium-resolution stellar occultation spectrometer measuring vertical profiles of ozone. In the first year after launch a large group of scientists performed additional measurements and validation activities to assess the quality of Envisat observations. In this paper, we present validation results of GOMOS ozone profiles from comparisons to microwave radiometer, balloon ozonesonde, and lidar measurements worldwide. Thirty-one instruments/launch sites at twenty-five stations ranging from the Arctic to the Antarctic joined in this activity. We identified 6747 collocated observations that were performed within an 800-km radius and a maximum 20-hour time difference of a satellite observation, for the period between 1 July 2002 and 1 April 2003. The GOMOS data analyzed here have been generated with a prototype processor that corresponds to version 4.02 of the operational GOMOS processor. The GOMOS data initially contained many obviously unrealistic values, most of which were successfully removed by imposing data quality criteria. Analyzing the effect of these criteria indicated, among other things, that for some specific stars, only less than 10% of their occultations yield an acceptable profile. The total number of useful collocated observations was reduced to 2502 because of GOMOS data unavailability, the imposed data quality criteria, and lack of altitude overlap. These collocated profiles were compared, and the results were analyzed for possible dependencies on several geophysical (e.g., latitude) and GOMOS observational (e.g., star characteristics) parameters. We find that GOMOS data quality is strongly dependent on the illumination of the limb through which the star is observed. Data measured under bright limb conditions, and to a certain extent also in twilight limb, should be used with caution, as their usability is doubtful. In dark limb the GOMOS data agree very well with the correlative data, and between 14- and 64-km altitude their differences only show a small (2.5–7.5%) insignificant negative bias with a standard deviation of 11–16% (19–63 km). This conclusion was demonstrated to be independent of the star temperature and magnitude and the latitudinal region of the GOMOS observation, with the exception of a slightly larger bias in the polar regions at altitudes between 35 and 45 km.

We have mapped Antarctic blue-ice areas using the U.S. National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Antarctica cloud-free image mosaic established by the United States Geological Survey. The mosaic consists of 38 scenes acquired from 1980 to 1994. Our results show that approximately 60 000 km2 of blue ice exist for each of the two main types of blue ice: “melt-induced” and “wind-induced”. Normally, the former type is located on slopes in coastal areas where climate conditions (i.e. persistent winds and temperature), together with favourable surface orientation, sustain conditions for surface and near surface melt. The latter blue-ice category occurs near mountains or on outlet glaciers, often at higher elevations, where persistent winds erode snow away year-round, and combined with sublimation creates areas of net ablation. Furthermore, we have identified an additional area of 121 000 km2 as having potential for blue ice. However, in these areas features such as mixed pixels, glazed snow surfaces, crevasses and/or shadows make interpretation more uncertain. In conclusion, a conservative estimate of Antarctic blue-ice area coverage by this method is found to be 120 000 km2 (∼0.8% of the Antarctic continent), with a potential maximum of 241 000 km2 (∼1.6% of the Antarctic continent).

Several years of total ozone measured from space by the ERS-2 GOME, the Earth Probe TOMS, and the ADEOS TOMS, are compared with high-quality ground-based observations associated with the Network for the Detection of Stratospheric Change (NDSC), over an extended latitude range and a variety of geophysical conditions. The comparisons with each spaceborne sensor are combined altogether for investigating their respective solar zenith angle (SZA) dependence, dispersion, and difference of sensitivity. The space- and ground-based data are found to agree within a few percent on average. However, the analysis highlights for both GOME and TOMS several sources of discrepancies: (i) a SZA dependence with TOMS beyond 80° SZA; (ii) a seasonal SZA dependence with GOME beyond 70° SZA; (iii) a difference of sensitivity with GOME at high latitudes; (iv) a difference of sensitivity to low ozone values between satellite and SAOZ sensors around the southern tropics; (v) a north/south difference of TOMS with the ground-based observations; and (vi) internal inconsistencies in GOME total ozone.