Earth system models have been continously improved over the past decades, but systematic
errors compared with observations and uncertainties in climate projections remain. This is …

Robust integration of physical knowledge and data is key to improve computational
simulations such as Earth system models. Data assimilation is crucial for achieving this goal …

Global measurements of ocean p CO 2 are critical to monitor and understand changes in the
global carbon cycle. However, p CO 2 observations remain sparse as they are mostly …

Accurate representations of unknown and sub-grid physical processes through
parameterizations (or closure) in numerical simulations with quantified uncertainty are …

Weather and climate forecasting is vital for sectors such as agriculture and disaster
management. Although numerical weather prediction (NWP) systems have advanced …

We present ConDiff, a novel dataset for scientific machine learning. ConDiff focuses on the
diffusion equation with varying coefficients, a fundamental problem in many applications of …

Detecting and attributing temperature increases due to climate change is crucial for
understanding global warming and guiding adaptation strategies. The complexity of …

We address the essential role of information retrieval in enhancing climate downscaling,
focusing on the need for high-resolution datasets and the application of deep learning …