27 citations · 45 across the 17 of their papers we have counts for
6 papers · 1 filter
Extrapolation from historical data cannot reliably predict the time of a potential AMOC collapse
Andreas Morr, Maya Ben-Yami, Brian Groenke +6
Ditlevsen and Ditlevsen [Nature Communications, 2023] (DD23 hereafter) propose a statistical framework to estimate the timing of a potential collapse of the Atlantic Meridional Ove…
Generative deep learning improves reconstruction of global historical climate records
Zhen Qian, Teng Liu, Sebastian Bathiany +7
Accurate assessment of anthropogenic climate change relies on historical instrumental data, yet observations from the early 20th century are sparse, fragmented, and uncertain. Conv…
Decadal sink-source shifts of forest aboveground carbon since 1988
Zhen Qian, Sebastian Bathiany, Teng Liu +3
Forest ecosystems are vital to the global carbon cycle, yet their long-term aboveground carbon (AGC) dynamics remain uncertain. Here, we integrate multi-source satellite observatio…
Conditional diffusion models for downscaling and bias correction of Earth system model precipitation
Michael Aich, Philipp Hess, Baoxiang Pan +3
Climate change exacerbates extreme weather events like heavy rainfall and flooding. As these events cause severe socioeconomic damage, accurate high-resolution simulation of precip…
Spatial correlation increase in single-sensor satellite data reveals loss of Amazon rainforest resilience
Lana L. Blaschke, Da Nian, Bathiany +4
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 resili…
Uncertainties too large to predict tipping times of major Earth system components from historical data
Maya Ben-Yami, Andreas Morr, Sebastian Bathiany +1
One way to warn of forthcoming critical transitions in Earth system components is using observations to detect declining system stability. It has also been suggested to extrapolate…