Zobrazeno 1 - 10
of 357
pro vyhledávání: '"Boers, Niklas"'
Nonlinear dynamical systems exposed to changing forcing can exhibit catastrophic transitions between alternative and often markedly different states. The phenomenon of critical slowing down (CSD) can be used to anticipate such transitions if caused b
Externí odkaz:
http://arxiv.org/abs/2409.07590
Numerical models of complex systems like the Earth system are expensive to run and involve many uncertain and typically hand-tuned parameters. In the context of anthropogenic climate change, there is particular concern that specific tipping elements,
Externí odkaz:
http://arxiv.org/abs/2409.04063
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
A deterministic dynamical system that slowly passes through a generic fold-type (saddle-node) bifurcation can be reduced to one-dimensional dynamics close to the bifurcation because of the centre manifold theorem. It is often tacitly assumed that the
Externí odkaz:
http://arxiv.org/abs/2311.18597