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.
Keywords: Box–Jenkins; ERA5; Multiple linear regression; Passive remote sensors; Time series.