6 citations · 7 across the 5 of their papers we have counts for
7 papers
CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling -Experiment Design and Overview
Neelesh Rampal, José González-Abad, Henry Addison +34
Machine learning (ML) has emerged as a cost-effective approach to complement dynamical downscaling for producing high-resolution regional climate projections. However, the absence…
Probabilistic storyline attribution using machine learning
Frieder Loer, Maybritt Schillinger, Sebastian Sippel
A fundamental goal in climate attribution is to estimate how forced climate change contributes to observed extreme weather events. The storyline attribution method compares an obse…
How Can Machine Learning Emulators Best Support Climate Science?
Luca Schmidt, Nina Effenberger, Vitus Benson +5
For decades, physics-based climate models have been used to provide insights for climate decision-making. Their application is, however, constrained by significant computational an…
Bridging CORDEX and CMIP6: Machine Learning Downscaling for Wind and Solar Energy Droughts in Central Europe
Nina Effenberger, Maxim Samarin, Maybritt Schillinger +1
Reliable regional climate information is essential for assessing the impacts of climate change and for planning in sectors such as renewable energy; yet, producing high-resolution…
EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
Maybritt Schillinger, Maxim Samarin, Xinwei Shen +2
The practical use of future climate projections from global circulation models (GCMs) is often limited by their coarse spatial resolution, requiring downscaling to generate high-re…
Enforcing tail calibration when training probabilistic forecast models
Jakob Benjamin Wessel, Maybritt Schillinger, Frank Kwasniok +1
Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated by optimizing a proper scoring rule o…
Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework
Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl +1
Earth's temperature variability can be partitioned into internal and externally-forced components. Yet, underlying mechanisms and their relative contributions remain insufficiently…