Identification and quantification of possible drivers of recent climate variability remain a challenging task. This important issue is addressed adopting a non-parametric information theory technique, the Transfer Entropy and its normalized variant. It distinctly quantifies actual information exchanged along with the directional flow of information between any two variables with no bearing on their common history or inputs, unlike correlation, mutual information etc. Measurements of greenhouse gases, CO2, CH4, and N2O; volcanic aerosols; solar activity: UV radiation, total solar irradiance (TSI ) and cosmic ray flux (CR); El Nino Southern Oscillation (ENSO) and Global Mean Temperature Anomaly (GMTA) made during 1984-2005 are utilized to distinguish driving and responding climate signals. Estimates of their relative contributions reveal that CO 2 (~24%), CH 4 (~19%) and volcanic aerosols (~23%) are the primary contributors to the observed variations in GMTA. While, UV (~9%) and ENSO (~12%) act as secondary drivers of variations in the GMTA, the remaining play a marginal role in the observed recent climate variability. Interestingly, ENSO and GMTA mutually drive each other at varied time lags. This study assists future modelling efforts in climate science.
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates（Sorbonne大学科学与工程系）；Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India（印度统计研究院应用数学与物理单元）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, 75005, France（Sorbonne大学人工智能中心）；Indian Institute of Technology Gandhinagar, Gandhinagar 382355, India（印度理工学院冈丁加尔分校）；National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Vasco da Gama, 403804, India（国家极地与海洋研究中心）；Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad-500032, India（国际信息技术研究所计算自然科学与生物信息中心）；Department of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90–537 Lodz, Poland（Lodz技术大学动力学系）；Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India（贾瓦德普大学数学生物学与生态中心）
Climate network and complexity based ENSO forecast for 2026
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association（波茨坦气候影响研究所（PIK），莱布尼茨协会成员）；State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所地球系统数值模拟与应用国家重点实验室）；School of Systems Science, Beijing Normal University（北京师范大学系统科学学院）；Institute for Theoretical Physics, Justus Liebig University Giessen（吉森大学约翰内斯·古腾堡理论物理研究所）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）
Colorado State University（科罗拉多州立大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Met Office Hadley Centre（英国气象局哈德莱中心）；University of Leeds（利兹大学）
