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Abstract Massive calving events result in significant instantaneous ice loss from Antarctica. The rarity and stochastic nature of these extreme events makes it difficult to understand their physical drivers, temporal trends, and future likelihood. To address this challenge, we turn to extreme value theory to investigate past trends in annual maxima iceberg area and assess the likelihood of high‐magnitude calving events. We use 47 years of iceberg size from satellite observations. Our analysis reveals no upward trend in the surface area of the largest annual iceberg over this time frame. This finding suggests that extreme calving events such as the recent 2017 Larsen C iceberg, A68, are statistically unexceptional and that extreme calving events are not necessarily a consequence of climate change. Nevertheless, it is statistically possible for Antarctica to experience a calving event up to several times greater than any in the observational record.
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An Effective Trend Surface Fitting Framework for Spatial Analysis of Extreme Events
Abstract The estimation of exceedance probabilities for extreme climatic events is critical for infrastructure design and risk assessment. Climatic events occur over a greater space than they are measured with point‐scale in situ gauges. In extreme value theory, the block maxima approach for spatial analysis of extremes depends on properly modeling the spatially varying Generalized Extreme Value marginal parameters (i.e., trend surfaces). Fitting these trend surfaces can be challenging since there are numerous spatial and temporal covariates that are potentially relevant for any given event type and region. Traditionally, covariate selection is based on assumptions regarding the topmost relevant drivers of the event. This work demonstrates the benefit of utilizing elastic‐net regression to support automatic selection from a relatively large set of physically relevant covariates during trend surface estimation. The trend surfaces presented are based on 24‐hr annual maximum precipitation for northeastern Colorado and the Texas‐Louisiana Gulf Coast.
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- PAR ID:
- 10444522
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 49
- Issue:
- 11
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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