Dataset for: A Model for Antarctic Surface Mass Balance and Ice Core Site Selection

• Main Authors: White, Philip Andrew, C. Shane Reese, Christensen, William F., Rupper, Summer
• Format: Dataset
• Language: unknown
• Published: Wiley 2019
• Subjects: Environmental Science; Antarctic; The Antarctic; Antarc*; ice core
• Online Access: https://dx.doi.org/10.6084/m9.figshare.8044745.v1; https://wiley.figshare.com/articles/Dataset_for_A_Model_for_Antarctic_Surface_Mass_Balance_and_Ice_Core_Site_Selection/8044745/1

In this study, we develop a model for Antarctic surface mass balance (SMB) that allows us to assess regional and global uncertainty in SMB estimation and carry out a model-based design to propose new measurement sites. For this analysis, we use a quality-controlled aggregate dataset of SMB field measurements with significantly more observations than previous analyses; however, many of the measurements in this dataset lack quality ratings. In addition, these data demonstrate spatial autocorrelation, heteroscedasticity, and non-Gaussianity. To account for these data attributes, we pose a Bayesian Gaussian process generalized linear model for SMB. To address missing reliability ratings, we use a mixture model with different variances to add robustness to our model. In addition, we present a novel approach for modeling the variance as a function of the mean to account for the heteroscedasticity in the data. Using this model, we predict Antarctic SMB and compare our estimates with previous estimates. In addition, we create prediction maps with uncertainty to visualize spatial patterns in SMB and to identify regions of high SMB uncertainty. Our model estimates total SMB to be 2156 Gton/yr over the range of our data, with 95\% credible interval (2081,2234) Gton/yr. Overall, our results suggest lower Antarctic SMB than previously reported. This lower SMB estimate may be indicative of a more dire diagnosis of the long-term health of the Antarctic ice sheets. Lastly, we use our model to propose 25 new measurement sites for field study utilizing a sequential design minimizing integrated mean squared error.