论文标题

单调西南雪密度曲线的分层整合空间过程建模

Hierarchical Integrated Spatial Process Modeling of Monotone West Antarctic Snow Density Curves

论文作者

White, Philip A., Keeler, Durban G., Rupper, Summer

论文摘要

表面以下的雪密度估计值,与飞机获得的冰期雷达测量结果相比,可提供特定于雪水积聚的现场历史。因为在整个南极洲的整个南极洲都钻雪核是不可行的,并且要了解气候变化如何影响世界上最大的淡水储层是至关重要的,所以我们开发的方法可以使雪密度估算在没有钻探雪的地区的区域中具有不确定性。 在内陆西南极洲,除了可能的微尺度变异性或测量误差外,雪密度随着深度的函数而单调增加,并且不能超过冰的密度。我们提出了一类新的集成空间过程模型,允许单调雪密度曲线插值。对于计算可行性,我们通过对数高斯空间过程的内核卷积构建空间深度过程。我们讨论模型比较,模型拟合和预测。使用此模型,我们将雪密度的估计值扩展到原始核心的深度之外,并估算尚未钻出雪芯的雪密度曲线。沿着带有冰渗透雷达的飞行线,我们使用插值的雪密度曲线来估计最近的水积聚,并发现最近几十年来主要减少水的积累。

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 micro-scale 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.

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