Zobrazeno 1 - 5
of 5
pro vyhledávání: '"S P, Bachman"'
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 14, Iss 3, Pp n/a-n/a (2022)
Abstract Ocean circulation models have systematic errors in large‐scale horizontal density gradients due to estimating the grid‐cell‐mean density by applying the nonlinear seawater equation of state to the grid‐cell‐mean water properties. I
Externí odkaz:
https://doaj.org/article/11f84344ef1d4e5abee1057279994d12
Autor:
I. Grooms, N. Loose, R. Abernathey, J. M. Steinberg, S. D. Bachman, G. Marques, A. P. Guillaumin, E. Yankovsky
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 9, Pp n/a-n/a (2021)
Abstract We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low‐pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian op
Externí odkaz:
https://doaj.org/article/d1a090f0eee947aaadb901cd44458b94
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 12, Pp n/a-n/a (2020)
Abstract Unresolved temperature and salinity fluctuations interact with a nonlinear seawater equation of state to produce significant errors in the ocean model evaluation of the large‐scale density field. It is shown that the impact of temperature
Externí odkaz:
https://doaj.org/article/70963d2014984e98855a937d2bea0d51
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 10, Pp n/a-n/a (2020)
Abstract The Gent–McWilliams parameterization is commonly used in global ocean models to model the advective component of tracer transport effected by unresolved mesoscale eddies. The vertical structure of the transfer coefficient in this parameter
Externí odkaz:
https://doaj.org/article/55f8e36747484c589c528efed61b87ec
Autor:
S, Bellot, Y, Lu, A, Antonelli, W J, Baker, J, Dransfield, F, Forest, W D, Kissling, I J, Leitch, E, Nic Lughadha, I, Ondo, S, Pironon, B E, Walker, R, Cámara-Leret, S P, Bachman
Publikováno v:
Nature ecologyevolution. 6(11)
Protecting nature's contributions to people requires accelerating extinction risk assessment and better integrating evolutionary, functional and used diversity with conservation planning. Here, we report machine learning extinction risk predictions f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::3854f52301a56568b77cc7f100ccb6a7
https://pubmed.ncbi.nlm.nih.gov/36163256
https://pubmed.ncbi.nlm.nih.gov/36163256