[1] Observations of snow accumulation rates from five new firn cores show a negative trend that is statistically significant over the past several decades across the central West Antarctic ice sheet (WAIS). A negative temporal trend in accumulation rates is unexpected in light of rising surface temperatures as well as model simulations predicting higher accumulation rates for the region. Both the magnitude of the mean accumulation rates and the range of interannual variability observed in the new records compare favorably to older records collected from a broad area of the WAIS, suggesting that the new data may serve as a regional proxy for recent temporal trends in West Antarctic accumulation rates. The observed negative trend in accumulation is likely the result of a shift in low-pressure systems over the Amundsen Sea region, dominated by changes in the austral fall season. Regional-scale climate models and reanalysis data do not capture the negative trend in accumulation rate observed in these firn cores. Nevertheless the models and reanalyses agree well in both accumulation-rate means and interannual variability, with no single model or dataset standing out as significantly more skilled at capturing the observed magnitude of and trend in accumulation rates in this region of the WAIS.

No abstract

Snow density estimates below the surface, used with airplane-acquired ice-penetrating radar measurements, give a site-specific history of snow water accumulation. Because it is infeasible to drill snow cores across all of Antarctica to measure snow density and because it is critical to understand how climatic changes are affecting the world's largest freshwater reservoir, we develop methods that enable snow density estimation with uncertainty in regions where snow cores have not been drilled.In inland West Antarctica, snow density increases monotonically as a function of depth, except for possible microscale variability or measurement error, and it cannot exceed the density of ice. We present a novel class of integrated spatial process models that allow interpolation of monotone snow density curves. For computational feasibility we construct the space-depth process through kernel convolutions of log-Gaussian spatial processes. We discuss model comparison, model fitting and prediction. Using this model, we extend estimates of snow density beyond the depth of the original core and estimate snow density curves where snow cores have not been drilled. Along flight lines with ice-penetrating radar, we use interpolated snow density curves to estimate recent water accumulation and find predominantly decreasing water accumulation over recent decades.

Pakistan is the most glaciated country on the planet but faces increasing water scarcity due to the vulnerability of its primary water source, the Indus River, to changes in climate and demand. Glacier melt constitutes over one-third of the Indus River’s discharge, but the impacts of glacier shrinkage from anthropogenic climate change are not equal across all eleven subbasins of the Upper Indus. We present an exploration of glacier melt contribution to Indus River flow at the subbasin scale using a distributed surface energy and mass balance model run 2001–2013 and calibrated with geodetic mass balance data. We find that the northern subbasins, the three in the Karakoram Range, contribute more glacier meltwater than the other basins combined. While glacier melt discharge tends to be large where there are more glaciers, our modeling study reveals that glacier melt does not scale directly with glaciated area. The largest volume of glacier melt comes from the Gilgit/Hunza subbasin, whose glaciers are at lower elevations than the other Karakoram subbasins. Regional application of the model allows an assessment of the dominant drivers of melt and their spatial distributions. Melt energy in the Nubra/Shyok and neighboring Zaskar subbasins is dominated by radiative fluxes, while turbulent fluxes dominate the melt signal in the west and south. This study provides a theoretical exploration of the spatial patterns to glacier melt in the Upper Indus Basin, a critical foundation for understanding when glaciers melt, information that can inform projections of water supply and scarcity in Pakistan.

No abstract

Snow density estimates as a function of depth are used for understanding climate processes, evaluating water accumulation trends in polar regions, and estimating glacier mass balances. The common and interpretable physically derived differential equation models for snow density are piecewise linear as a function of depth (on a transformed scale); thus, they can fail to capture important data features. Moreover, the differential equation parameters show strong spatial autocorrelation. To address these issues, we allow the parameters of the physical model, including random change points over depth, to vary spatially.We also develop a framework for functionally smoothing the physically motivated model. To preserve inference on the interpretable physical model, we project the smoothing function into the physical model's spatially varying null space. The proposed spatially and functionally smoothed snow density model better fits the data while preserving inference on physical parameters. Using this model, we find significant spatial variation in the parameters that govern snow densification.

In many settings, data acquisition generates outliers that can obscure inference. Therefore, practitioners often either identify and remove outliers or accommodate outliers using robust models. However, identifying and removing outliers is often an ad hoc process that affects inference, and robust methods are often too simple for some applications. In our motivating application, scientists drill snow cores and measure snow density to infer densification rates that aid in estimating snow water accumulation rates and glacier mass balances. Advanced measurement techniques can measure density at high resolution over depth but are sensitive to core imperfections, making them prone to outliers. Outlier accommodation is challenging in this setting because the distribution of outliers evolves over depth and the data demonstrate natural heteroscedasticity. To address these challenges, we present a two-component mixture model using a physically motivated snow density model and an outlier model, both of which evolve over depth. The physical component of the mixture model has a mean function with normally distributed depth-dependent heteroscedastic errors. The outlier component is specified using a semiparametric prior density process constructed through a normalized process convolution of log-normal random variables. We demonstrate that this model outperforms alternatives and can be used for various inferential tasks.

Glacial ogives are transverse topographic, wave-like surface features that form below icefalls on some alpine glaciers. Groundpenetrating radar surveys from the Gorner glacier system in the Swiss Alps reveal an along-fl ow periodicity in scattering intensity that correlates with ogives. The scattering appears in the ablation zone and occurs at 5-20 m depth. The geometry of the scattering mimics that of the ogives, although exaggerated in amplitude. We interpret the scattering to represent lateral variations in water content. We propose that as glacial ice accelerated and stretched through the icefall, seasonal fl uctuations occurred in water infi ltration to crevasses during the summer and subsequent freezing of that water in the crevasses in the winter. This seasonally varying infi lling and freezing locally altered the distribution of temperature, creating zones of temperate ice with water inclusions that preferentially scatter radar energy. In addition to the scattering pattern, highly refl ective planar features associated with these periodic regions of temperate ice are interpreted as water-fi lled fractures. A three-dimensional rendering of the orientation of these planar features precludes a "fold-and-thrust" hypothesis for the formation of the ogives.