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Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models
Climate change has a large influence on the occurrence of extreme hydrological events. However, r...
SM Hauswirth, K van der Wiel, MFP Bierkens, V Beijk, N Wanders | Journal: Frontiers in Water | Volume: 5 | Year: 2023 | First page: 11008108 | doi: 10.3389/frwa.2023.1108108
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From ship smokestack to global air pollution: bridging the scales to better constrain ship NOx emissions from space
Seagoing ships combust massive amounts of
marine heavy fuel, which leads to significant
emissions...
GCM Vinken, KF Boersma | Journal: SOLAS Newsletter | Year: 2011 | First page: 8 | Last page: 0
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Supermodels: Dynamically Coupled Ensembles of Imperfect Models
At a dozen or so institutes around the world, comprehensive climate models are being developed an...
W Wiegerinck, FM Selten | Conference: Conference on Artificial Intelligence (AAAI-12) | Organisation: AAAI | Year: 2012 | First page: 0 | Last page: 0
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Linearity aspects of the ensemble of data assimilations technique
We examine the linearity of the Ensemble of Data Assimilations (EDA) technique with
respect to th...
L Megner, DGH Tan, H Kornich, A Horanyi, A Stoffelen, GJ Marseille | Status: published | Journal: Quart. J. Royal Meteor. Soc. | Year: 2014 | doi: DOI:10.1002/qj.2362
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Clouds, Circulation and Climate Sensitivity
Fundamental puzzles of climate science remain unsolved because of our limited understanding of ho...
S Bony, B Stevens, DMW Frierson, C Jakob, M Kageyama, R Pincus, TG Shepherd, SC Sherwood, AP Siebesma, AH Sobel, M Watanabe, MJ Webb | Status: published | Journal: Nature Geoscience | Volume: 8 | Year: 2015 | First page: 261 | Last page: 268 | doi: doi:10.1038/ngeo2398
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