All Outputs (2)

State-of-the-art global models underestimate impacts from climate extremes (2019)
Journal Article
Schewe, J., Gosling, S. N., Reyer, C., Zhao, F., Ciais, P., Elliott, J., …Warszawski, L. (2019). State-of-the-art global models underestimate impacts from climate extremes. Nature Communications, 10, 1-14. https://doi.org/10.1038/s41467-019-08745-6

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically ac... Read More about State-of-the-art global models underestimate impacts from climate extremes.

Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models (2019)
Journal Article
Zaherpour, J., Mount, N., Gosling, S., Dankers, R., Eisner, S., Gerten, D., …Wada, Y. (2019). Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models. Environmental Modelling and Software, 114, 112-128. https://doi.org/10.1016/j.envsoft.2019.01.003

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global... Read More about Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models.