Abstract
The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p = 0.04 ) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p = 0.05 ). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p = 0.01 ∼ 0.27 ) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p = 0.04 ∼ 0.42 ) with a peak over the higher latitudes.
Highlights
Variation in surface air temperature (SAT) over the Antarctic is an indicator of global SAT change [1]
82 stationbased SAT data over the Antarctic continent and its surrounding regions were selected to estimate the monthly SAT fields during the period 1960–2019 using the multiple linear regression (MLR), backpropagation artificial neural network (BPANN), and genetic algorithm (GA)-BPANN methods. e accuracies of the three interpolation methods were tested by cross-validation. e annual and monthly mean absolute error (MAE) and root-mean-square error (RMSE) were calculated and used to observe the variations of the interpolation accuracies of the three methods (Figures 4 and 5)
Following GA-BPANN is the MLR interpolation method, with station-averaged MAEs in each month ranging from 3.61 to 6.35 and a mean of 5.32 and station-averaged RMSEs in each month ranging from 6.84 to 10.38 and a mean of 9.03. e MAEs and RMSEs associated with BPANN are greater than those of GA-BPANN and MLR, e.g., the station
Summary
Variation in surface air temperature (SAT) over the Antarctic is an indicator of global SAT change [1]. Some studies have reported that most of the Antarctic continent has shown a cooling trend [5,6,7]. Among the factors responsible for the inconsistent study results are the sparsity and temporal discontinuity of stationbased SAT data [14], the lack of good quality satellite-based SAT products, which are often affected by cloud cover and snow surface over the Antarctic [15], the large warm bias in reproducing the Antarctic temperature using climate models [16], the clear cold bias for monthly SAT reanalysis datasets over the Antarctic coastal regions, and the large warm bias dominating the winter over the Antarctic inland [17].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
