Zobrazeno 1 - 10
of 326
pro vyhledávání: '"Zanna, Laure"'
Autor:
Gultekin, Cem, Subel, Adam, Zhang, Cheng, Leibovich, Matan, Perezhogin, Pavel, Adcroft, Alistair, Fernandez-Granda, Carlos, Zanna, Laure
Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the climate system. Parameterization is an approach to capture the effect
Externí odkaz:
http://arxiv.org/abs/2411.06604
This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023). We focus on the boundary con
Externí odkaz:
http://arxiv.org/abs/2411.01138
Autor:
Yang, Qidong, Zhu, Weicheng, Keslin, Joseph, Zanna, Laure, Rudner, Tim G. J., Fernandez-Granda, Carlos
Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often desirable to quantify the uncertainty associated with the prediction (in
Externí odkaz:
http://arxiv.org/abs/2410.23272
The energy surplus resulting from radiative forcing causes warming of the Earth system. This initial warming drives a myriad of changes including in sea surface temperatures (SSTs), leading to different radiative feedbacks. The relationship between t
Externí odkaz:
http://arxiv.org/abs/2408.12585
Autor:
Dheeshjith, Surya, Subel, Adam, Gupta, Shubham, Adcroft, Alistair, Fernandez-Granda, Carlos, Busecke, Julius, Zanna, Laure
With the success of machine learning (ML) applied to climate reaching further every day, emulators have begun to show promise not only for weather but for multi-year time scales in the atmosphere. Similar work for the ocean remains nascent, with stat
Externí odkaz:
http://arxiv.org/abs/2405.18585
Autor:
Subel, Adam, Zanna, Laure
The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nascent despite the large potential for accelerating coupled climate simul
Externí odkaz:
http://arxiv.org/abs/2402.04342
The parameterizations of submesoscale ($<10$km) ocean surface flows are critical in capturing the subgrid effects of vertical fluxes in the ocean mixed layer, yet they struggle to infer the full-complexity of these fluxes in relation to the large sca
Externí odkaz:
http://arxiv.org/abs/2312.06972
Publikováno v:
Journal of Advances in Modeling Earth Systems, 16, e2023MS004104
Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be
Externí odkaz:
http://arxiv.org/abs/2311.02517
In this study we perform online sea ice bias correction within a GFDL global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023) for the purpose of predicting sea ice c
Externí odkaz:
http://arxiv.org/abs/2310.02488
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution simulations. However, when trained in a purely offline mode, integratin
Externí odkaz:
http://arxiv.org/abs/2307.13144