Bayesian structure learning for climate model evaluation

TJ O'Kane, D Harries, MA Collier - Journal of Advances in …, 2024 - Wiley Online Library
A Bayesian structure learning approach is employed to compare and contrast interactions
between the major climate teleconnections over the recent past as revealed in reanalyses …

[HTML][HTML] Probabilistic Causal Network Modeling of Southern Hemisphere Jet Subseasonal to Seasonal Predictability

E Saggioro, TG Shepherd, J Knight - Journal of Climate, 2024 - journals.ametsoc.org
Skillful prediction of the Southern Hemisphere (SH) eddy-driven jet is crucial for
representation of mid-to-high-latitude SH climate variability. In the austral spring-to-summer …

Dynamic Bayesian networks for evaluation of Granger causal relationships in climate reanalyses

D Harries, TJ O'Kane - Journal of Advances in Modeling Earth …, 2021 - Wiley Online Library
We apply a Bayesian structure learning approach to study interactions between global
climate modes, so illustrating its use as a framework for developing process‐based …

Causal network approaches for the study of sub-seasonal to seasonal variability and predictability

E Saggioro - 2023 - centaur.reading.ac.uk
Statistics is fundamental for climate science. It helps making sense of its complex and multi-
scale features by characterizing its aggregated behaviour. However, statistical methods …

[HTML][HTML] 关于遥相关问题的研究现状综述

王娜 - Geographical Science Research, 2023 - hanspub.org
地球科学中的气候系统是一个复杂的物理系统. 人类在远古时期就注意到了一些独特的自然现象
, 通过生活实践想要理解地球系统各个要素之间的联系, 也总结了一些规律, 比如瑞雪兆丰年等 …

[PDF][PDF] Quel est l'impact d'une fonte soudaine de la glace de mer arctique sur les températures extrêmes aux moyennes latitudes?

E Neimry, T Fichefet, F Massonnet - dial.uclouvain.be
ABSTRACT L'étendue de la glace de mer arctique diminue considérablement depuis le
début des observations satellitaires en 1979. Selon les projections climatiques, de plus en …