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Detection and attribution of large spatiotemporal extreme events in Earth observation data

2013

Article

ei


Latest climate projections suggest that both frequency and intensity of climate extremes will be substantially modified over the course of the coming decades. As a consequence, we need to understand to what extent and via which pathways climate extremes affect the state and functionality of terrestrial ecosystems and the associated biogeochemical cycles on a global scale. So far the impacts of climate extremes on the terrestrial biosphere were mainly investigated on the basis of case studies, while global assessments are widely lacking. In order to facilitate global analysis of this kind, we present a methodological framework that firstly detects spatiotemporally contiguous extremes in Earth observations, and secondly infers the likely pathway of the preceding climate anomaly. The approach does not require long time series, is computationally fast, and easily applicable to a variety of data sets with different spatial and temporal resolutions. The key element of our analysis strategy is to directly search in the relevant observations for spatiotemporally connected components exceeding a certain percentile threshold. We also put an emphasis on characterization of extreme event distribution, and scrutinize the attribution issue. We exemplify the analysis strategy by exploring the fraction of absorbed photosynthetically active radiation (fAPAR) from 1982 to 2011. Our results suggest that the hot spots of extremes in fAPAR lie in Northeastern Brazil, Southeastern Australia, Kenya and Tanzania. Moreover, we demonstrate that the size distribution of extremes follow a distinct power law. The attribution framework reveals that extremes in fAPAR are primarily driven by phases of water scarcity.

Author(s): Zscheischler, J. and Mahecha, MD. and Harmeling, S. and Reichstein, M.
Journal: Ecological Informatics
Volume: 15
Pages: 66-73
Year: 2013
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1016/j.ecoinf.2013.03.004

Links: Web

BibTex

@article{ZscheischlerMHR2013,
  title = {Detection and attribution of large spatiotemporal extreme events in Earth observation data},
  author = {Zscheischler, J. and Mahecha, MD. and Harmeling, S. and Reichstein, M.},
  journal = {Ecological Informatics},
  volume = {15},
  pages = {66-73},
  year = {2013}
}