Large-scale and regional climatic influences on surface temperature and precipitation in the South Shetland Islands, northern Antarctic Peninsula.

Using data from SCAR observations, ERA5 reanalysis, and regional climate model simulations (RACMO), we examined the influence of large- and regional-scale climate forcing on temperature and precipitation variations in the South Shetland Islands (SSI). Specifically, we focused on understanding how regional climate indices influence the temporal variability of temperature and precipitation on the SSI. Our findings indicate that both large- and regional-scale climate indices significantly impact the interannual and seasonal temperature variability in the SSI. For instance, the Amundsen Sea Low, characterised by low-pressure systems over the Amundsen Sea, and sea ice extent in the northwestern part of the Weddell Sea, exert a strong influence on temperature variability (r from -0.64 to -0.87; p < 0.05). In contrast, precipitation variability in this region is primarily controlled by regional climatic indices. Particularly, anomalies in atmospheric and surface pressure over the Drake Passage region strongly regulate the interannual variability of precipitation in the SSI (r from -0.46 to -0.70; p < 0.05). Large-scale climatic indices demonstrate low but statistically significant correlations, including the Southern Annular Mode and deep convection in the central tropical Pacific. Given the importance of temperature and precipitation in the glacier changes, we recommend assessing the impact of the Drake region on SSI glaciers.

Spectral Linear Mixing Model application in passive microwave data to analyze Antarctic surface melting dynamics (1978-2018).

Several studies have utilized passive microwave imagery for monitoring snowmelt in Antarctica. However, due to the low spatial resolution of these images (25 km), the quantification of snowmelt is not precise. To enhance the accuracy of these estimations, this study proposed a subpixel analysis approach based on a Spectral Linear Mixing Model. This approach was applied to images obtained from channels 18/19 GHz and 37 GHz, both horizontally and vertically polarized, acquired from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSM/IS) instruments, spanning the period 1978-2018. The spatiotemporal analysis of the estimated snowmelt fraction images indicated that the most persistent and intensive melt was observed on the Antarctic Peninsula, particularly on the Larsen, Wilkins, George VI, and Wordie ice shelves. The melting period in the Antarctic Peninsula began in late October, with a peak in early January, and ended in late March. Other regions with persistent and intensive snowmelt were Mary Bird Land and Wilkes Land, followed by Dronning Maud Land, Amery Ice Shelf, Filchner-Ronne Ice Shelf, and Ross Ice Shelf. These snowmelt data are valuable for modeling the impacts of snowmelt on glacial systems, local coastal environments, and sea-level rise.

Concentration and thickness of sea ice in the Weddell Sea from SSM/I passive microwave radiometer data.

This study evaluated feasibility statistically and analyzed, during the freezing period, the relationship between brightness temperature (Tb) data of the 37V polarisation and the GR3719 (Gradient Ratio 37V and 19V) obtained by Special Sensor Microwave/Imager from F11 and F13 satellites with sea ice thickness (SIT) data obtained in the Weddell Sea through Antarctic Sea Ice Processes and Climate program. The multiple linear regression (MLR) was applied at 1,520 points, with 70% of these points being randomly separated to generate the MLR and 30% to carry out the validation. To perform the temporal mapping, the MLR was applied only to pixels with sea ice concentration (SIC) ≥ 90%, obtained through the fraction image calculated from the spectral linear mixing model (SLMM) using the Tb in the channels and polarizations 19H, 19V and 37V. The results of the SLMM validation process for estimating the SIC were σ = 10.5%, RMSE = 11.0%, and bias = -2.8%, and the SIT based on the MLR, the results were R² = 0.57, RMSE = 0.268 m, and bias = 0.103 m. In the SIT mapping, we highlight the trend of thickness reduction on the east coast of the Antarctic Peninsula during the period 1992-2009.

Snowmelt retrieval algorithm for the Antarctic Peninsula using SAR imageries.

The classification of Synthetic Aperture Radar (SAR) images by knowledge-based algorithms with elevation and backscatter thresholds were used in several studies to detect the Wet Snow Radar Zone (WSZ) in the Antarctic Peninsula. To identify it more accurately based on its seasonal variations, this study proposed the additional use of a threshold in synthetic images, created by rationing summer and winter sigma linear images. In our algorithm we used the following thresholds to detect the WSZ in Envisat ASAR imageries, using the Radarsat Antarctic Map Digital Elevation Model as ancillary data: i) -25 dB < s0 < -14 dB; ii) slinear summer / slinear winter < 0.4; iii) elevation H < 1,200 m for northern tip and H < 800 m for southern tip of the Antarctic Peninsula. The classified images were post-processed by a focal majority 5 x 5 filter and superimposed by an image of rock outcrops derived from the Antarctic Digital Database. The ratio image threshold allowed discriminating the WSZ from the Dry Snow Radar Zone and radar shadows, as well as transitional areas between this glacier zone and the Frozen Percolation Radar Zone, which would be classified incorrectly if we used only elevation and backscatter thresholds.

Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula.

Sea ice is one of the main components of the cryosphere that modifies the exchange of heat and moisture between the ocean and atmosphere, regulating the global climate. In this sense, it is important to identify the concentration of sea ice in different regions of Antarctica in order to measure the impact of environmental changes on the region's ecosystem. The objective of this study was to evaluate the performance of the multiple linear regression and Box-Jenkins methods for predicting the concentration of sea ice along the northwest coast of the Antarctic Peninsula. Sea ice concentration data from May to November for the period 1979-2018 were extracted from passive remote sensors including a scanning multichannel microwave radiometer, special sensor microwave imager, and special sensor microwave imager/sounder. Meteorological variables from the atmospheric reanalysis model ERA5 of the European Center for Medium-Range Weather Forecasts were used as predictor variables, and the leave-one-out cross-validation technique was used to calibrate and validate the models. It was found that both statistical models have similar performance when analyzing residual analysis results, root mean square error of cross-validation, and final accuracy and residual standard deviation, these responses being related to the regionalization of the study area and to the Box-Jenkins presents strong, homogeneous, and stable correlations in the time series modeled for each pixel.