A model for Antarctic surface mass balance and ice core site selection

Abstract

In this study, we develop a model for Antarctic surface mass balance (SMB), which 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 data set of SMB field measurements with significantly more observations than previous analyses; however, many of the measurements in this data set 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 the total SMB to be 2,156 Gton/yr over the range of our data, with 95% credible interval (2,081, 2,234) 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 the field study utilizing a sequential design, minimizing the integrated mean squared error.

Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19

“…Second, representing spatio-temporal processes with a separable autoregressive model relies on the assumption that the spatial and temporal variabilities are uncoupled and that the temporal variability can be simply described with a first-order Markov process. These two assumptions may be too restrictive to describe the complex spatio-temporal variability of stochastic processes like the surface mass balance (White et al, 2019). Third, the SPDE and autoregressive models require the specification of model parameters, most notably the spatial and temporal correlation lengths.…”

Hierarchical integrated spatial process modeling of monotone West Antarctic snow density curves

“…The laser altimetry method, in particular, is a focus of several national and international laboratories and organizations, highlighted by NASA's recent ICESat-2 satellite launch in 2018 and the ongoing ESA CryoSat-2 laser altimeter mission (Tedesco et al (2014)). Lastly, to improve snow density estimation, we will consider extensions of White et al (2019), where we consider sites that would sequentially minimize integrated error criteria over regions of Antarctica.…”

Comment on gmd-2021-321

“…Second, representing spatiotemporal processes with a separable autoregressive model relies on the assumption that the spatial and temporal variabilities are uncoupled and that the temporal variability can be simply described with a first-order Markov process. These two assumptions may be too restrictive to describe the complex spatio-temporal variability of stochastic processes like the surface mass balance (White et al, 2019). Third, the SPDE and autoregressive models require the specification of model parameters, most notably the spatial and temporal correlation lengths.…”