Improved Methods for Analysis of Decadal Elevation-Change Time Series Over Antarctica

In this paper, several techniques to improve the processing and analysis of decadal elevation-change time series (ECTS) constructed using satellite radar altimeter data over the Antarctic ice sheet are described. First, a method that improves both the quality and quantity of ice-sheet ECTS is introduced. By dynamically selecting the reference month used in constructing ECTS for a given local region, the maximum number of elevation-change estimates are used in a matrix processing technique. This can improve ECTS quality while also generating ECTS in new areas, leading to a significant increase in spatial coverage. Next, an improved autoregressive (IAR) approach is presented for modeling nonlinear medium-period variations in decadal Antarctic ECTS. This builds upon previous work where an AR model was used to characterize seasonal and interannual variations in ice-sheet ECTS. The improved approach avoids overdecomposition by adopting an iterative local average filter to estimate nonlinear medium-period trends. Monte Carlo simulations show that the IAR method significantly outperforms the AR method when realistic nonlinear trends are present. Because of these characteristics, the IAR model is able to adequately characterize seasonal, interannual, and medium-period nonlinear signals present in decadal ECTS and extract their long-term trends with very small error. This is important for accurate measurement of decadal elevation change over the Antarctic ice sheet and for assessing the impact of these changes on the global sea level