Zobrazeno 1 - 10
of 338
pro vyhledávání: '"Boers, Niklas"'
Predicting chaotic dynamical systems is critical in many scientific fields such as weather prediction, but challenging due to the characterizing sensitive dependence on initial conditions. Traditional modeling approaches require extensive domain know
Externí odkaz:
http://arxiv.org/abs/2407.20158
Autor:
Hess, Philipp, Boers, Niklas
Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce errors and increase the resolution of ESMs. However, existing methods
Externí odkaz:
http://arxiv.org/abs/2406.15026
Autor:
Guo, Zijie, Lyu, Pumeng, Ling, Fenghua, Luo, Jing-Jia, Boers, Niklas, Ouyang, Wanli, Bai, Lei
Ocean dynamics plays a crucial role in driving global weather and climate patterns. Accurate and efficient modeling of ocean dynamics is essential for improved understanding of complex ocean circulation and processes, for predicting climate variation
Externí odkaz:
http://arxiv.org/abs/2405.15412
The Indian Summer Monsoon (ISM) and the West African Monsoon (WAM) are dominant drivers of boreal summer precipitation variability in tropical and subtropical regions. Although the regional precipitation dynamics in these two regions have been extens
Externí odkaz:
http://arxiv.org/abs/2405.08492
Climate change exacerbates extreme weather events like heavy rainfall and flooding. As these events cause severe losses of property and lives, accurate high-resolution simulation of precipitation is imperative. However, existing Earth System Models (
Externí odkaz:
http://arxiv.org/abs/2404.14416
Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive. Recent machine learning approaches have shown
Externí odkaz:
http://arxiv.org/abs/2403.02774
Recent studies have shown that deep learning (DL) models can skillfully predict the El Ni\~no-Southern Oscillation (ENSO) forecasts over 1.5 years ahead. However, concerns regarding the reliability of predictions made by DL methods persist, including
Externí odkaz:
http://arxiv.org/abs/2312.10429
Autor:
Blaschke, Lana L., Nian, Da, Bathiany, Ben-Yami, Maya, Smith, Taylor, Boulton, Chris A., Boers, Niklas
The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna-like state. Previous work suggesting declining resilience in recent decades was based only on local resilience indicators.
Externí odkaz:
http://arxiv.org/abs/2310.18540
Autor:
Benson, Vitus, Donges, Jonathan F., Boers, Niklas, Hirota, Marina, Morr, Andreas, Staal, Arie, Vollmer, Jürgen, Wunderling, Nico
Publikováno v:
Environ. Res. Lett. 19 (2024) 024029
The Amazon rainforest is considered one of the Earth's tipping elements and may lose stability under ongoing climate change. Recently a decrease in tropical rainforest resilience has been identified globally from remotely sensed vegetation data. Howe
Externí odkaz:
http://arxiv.org/abs/2310.16021
Autor:
Morr, Andreas, Boers, Niklas
Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicat
Externí odkaz:
http://arxiv.org/abs/2310.05587