4 citations · 5 across the 9 of their papers we have counts for
11 papers
Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
Simon Adamov, Oliver Fuhrer, Reto Knutti +1
Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at far lower inference cost. Many…
Statistical Noise and Missing Forcing Limit Estimates of Earth's Feedback from Prescribed Sea-Surface Temperature Simulations
Gergana Gyuleva, Reto Knutti, Robin Noyelle +3
Earth's feedback parameter measures how the Earth system responds to forcing and is inversely proportional to climate sensitivity. Sea-surface temperature (SST) patterns can modula…
Uncertainty in wind and solar projections depends on global and regional climate models
Nina Effenberger, Reto Knutti
Ensembles of regional-global climate model combinations show substantial spread in projected wind and solar resources. Using 31 RCM-GCM pairs, we quantify the sources of this sprea…
AI-Assisted Scientific Assessment: A Case Study on Climate Change
Christian Buck, Levke Caesar, Michelle Chen Huebscher +13
The emerging paradigm of AI co-scientists focuses on tasks characterized by repeatable verification, where agents explore search spaces in 'guess and check' loops. This paradigm do…
Climate change impacts on net load under technological uncertainty in European power systems
Luna Bloin-Wibe, Erich Fischer, Leonard Göke +3
Renewable energy sources play a major role in future net-zero energy systems. However, achieving energy system resilience remains challenging, since renewables depend on weather fl…
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…
CLINB: A Climate Intelligence Benchmark for Foundational Models
Michelle Chen Huebscher, Katharine Mach, Aleksandar Stanić +10
Evaluating how Large Language Models (LLMs) handle complex, specialized knowledge remains a critical challenge. We address this through the lens of climate change by introducing CL…
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…
Climate2Energy: a framework to consistently include climate change into energy system modeling
Jan Wohland, Luna Bloin-Wibe, Erich Fischer +5
Supply and demand in future energy systems depend on the weather. We therefore need to quantify how climate change and variability impact energy systems. Here, we present Climate2E…
Robust detection and attribution of climate change under interventions
Enikő Székely, Sebastian Sippel, Nicolai Meinshausen +2
Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variabil…
A direct approach to detection and attribution of climate change
Eniko Székely, Sebastian Sippel, Reto Knutti +2
We present here a novel statistical learning approach for detection and attribution (D&A) of climate change. Traditional optimal D&A studies try to directly model the observations…