Zobrazeno 1 - 10
of 34 158
pro vyhledávání: '"Earth System Models"'
Autor:
Ullrich, Paul A., Barnes, Elizabeth A., Collins, William D., Dagon, Katherine, Duan, Shiheng, Elms, Joshua, Lee, Jiwoo, Leung, L. Ruby, Lu, Dan, Molina, Maria J., O'Brien, Travis A.
Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics-based
Externí odkaz:
http://arxiv.org/abs/2410.19882
This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and
Externí odkaz:
http://arxiv.org/abs/2409.18011
Earth System Models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analys
Externí odkaz:
http://arxiv.org/abs/2409.11601
Autor:
DelSole, Timothy1 (AUTHOR) tdelsole@gmu.edu, Tippett, Michael K.2 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Dec2024, Vol. 16 Issue 12, p1-26. 26p.
Autor:
Swaminathan, Ranjini1,2 (AUTHOR) r.swaminathan@reading.ac.uk, Quaife, Tristan1,2 (AUTHOR), Allan, Richard1,2 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Jul2024, Vol. 16 Issue 7, p1-16. 16p.
Autor:
Li, Fang1 (AUTHOR) lifang@mail.iap.ac.cn, Song, Xiang1 (AUTHOR), Harrison, Sandy P.2 (AUTHOR), Marlon, Jennifer R.3 (AUTHOR), Lin, Zhongda4 (AUTHOR), Leung, L. Ruby5 (AUTHOR), Schwinger, Jörg6 (AUTHOR), Marécal, Virginie7 (AUTHOR), Wang, Shiyu8 (AUTHOR), Ward, Daniel S.9 (AUTHOR), Dong, Xiao1 (AUTHOR), Lee, Hanna10 (AUTHOR), Nieradzik, Lars11 (AUTHOR), Rabin, Sam S.12 (AUTHOR), Séférian, Roland7 (AUTHOR)
Publikováno v:
Geoscientific Model Development. 2024, Vol. 17 Issue 23, p8751-8771. 21p.
Autor:
Hess, Philipp, Boers, Niklas
Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce errors and increase the resolution of ESMs. However, existing methods
Externí odkaz:
http://arxiv.org/abs/2406.15026
Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale. Generative deep learning approaches are suitable for emulat
Externí odkaz:
http://arxiv.org/abs/2404.08797
Autor:
Behrens, Gunnar, Beucler, Tom, Iglesias-Suarez, Fernando, Yu, Sungduk, Gentine, Pierre, Pritchard, Michael, Schwabe, Mierk, Eyring, Veronika
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty
Externí odkaz:
http://arxiv.org/abs/2402.03079
Autor:
Yu, Yangyang 1, Zhang, Shaoqing 1, 2, 3, 9, ∗, Fu, Haohuan 4, 5, ∗∗, Chen, Dexun 5, ∗∗∗, Gao, Yang 3, 6, Lin, Xiaopei 1, 2, 3, Liu, Zhao 5, 7, Lv, Xiaojing 5, 8
Publikováno v:
In iScience 17 January 2025 28(1)