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Elke Zeller

Researcher at Pusan National University

Publications -  8
Citations -  635

Elke Zeller is an academic researcher from Pusan National University. The author has contributed to research in topics: Climate change & Computer science. The author has an hindex of 1, co-authored 2 publications receiving 360 citations.

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Journal ArticleDOI

El Niño–Southern Oscillation complexity

Axel Timmermann, +50 more
- 26 Jul 2018 - 
TL;DR: A synopsis of the current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system is provided and a unifying framework that identifies the key factors for this complexity is proposed.
Journal ArticleDOI

Climate effects on archaic human habitats and species successions

TL;DR: This paper used an unprecedented transient Pleistocene coupled general circulation model simulation in combination with an extensive compilation of fossil and archaeological records to study the spatio-temporal habitat suitability for five hominin species over the past 2 million years.
Journal ArticleDOI

Tropical Indo-Pacific SST influences on vegetation variability in eastern Africa.

TL;DR: In this paper, a suite of idealized Earth system model simulations is used to elucidate the governing processes for eastern African interannual vegetation changes, and the authors focus on Tanzania, showing that the 2-year-long vegetation decline in Tanzania during an ENSO cycle can be explained as a double-integration of the local rainfall anomalies, which originate from the seasonally-modulated EnsO Pacific-SST forcing (Combination mode).
Proceedings ArticleDOI

Downscaling Earth System Models with Deep Learning

TL;DR: In this paper , a new method for downscaling climate simulations called GINE (Geospatial INformation Encoded Statistical Downscaling) is presented, which applies the latest computer vision techniques over topography-driven spatial and local-level information.
Proceedings ArticleDOI

Neural Classification of Terrestrial Biomes

TL;DR: In this paper , the authors employed multiple deep models to classify biomes with the goal of predicting future changes in vegetation and observed that the use of additional climate variables helps improve the prediction accuracy without overfitting the data.