Antarctic sea ice, a key component in the complex Antarctic climate system, is an important driver and indicator of the global climate. In the relatively short satellite-observed period from 1979 to 2022 the sea ice extent has continuously increased …
Antarctic sea ice extent reached a new record low of 1.965 million km2 on 23 February 2022. This extent is approximately 32% below climatological values and might indicate a transition to new, more extreme, annual fluctuations.
In stark contrast to the Arctic, there have been statistically significant positive trends in total Antarctic sea ice extent since 1979. However, the short and highly variable nature of observed Antarctic sea ice extent limits the ability to fully …
Landfast sea ice (fast ice) is an important though poorly understood component of the cryosphere on the Antarctic continental shelf, where it plays a key role in atmosphere–ocean–ice-sheet interaction and coupled ecological and biogeochemical …
Understanding the variability of Antarctic sea ice is an ongoing challenge given the limitations of observed data. Coupled climate model simulations present the opportunity to examine this variability in Antarctic sea ice. Here, the daily sea ice …
The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and reaching its minimum in February. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each …
The traditional best linear unbiased prediction procedure ('Kriging') is used in this paper for inference, but within a Bayesian framework. See Brown, Le and Zidek (1994) for an alternative Bayesian formulation. Our approach is to exam how posterior …
In this article we develop a random field model for the mean temperature over the region in the northern United States covering eastern Montana through the Dakotas and northern Nebraska up to the Canadian border. The readings are temperatures at the …
This is the rejoinder to the discussion of 'An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields' (https://doi.org/10.1080/01621459.1994.10476754)
This article is concerned with predicting for Gaussian random fields in a way that appropriately deals with uncertainty in the covariance function. To this end, we analyze the best linear unbiased prediction procedure within a Bayesian framework. …