Editorial board
Editors of this journal work on a purely voluntary basis without remuneration in line with the not-for-profit philosophy of the EGU.
Executive editors
Christian Franzke
Pusan National University
Center for Climate Physics, Institute for Basic Science
Climate System
Center for Climate Physics, Institute for Basic Science
Climate System
Korea, Republic Of
Subject areas
Subject areas
Time series, machine learning, networks, stochastic processes, extreme events
Ana M. Mancho
Consejo Superior de Investigaciones Científicas
ICMAT
ICMAT
Spain
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Daniel Schertzer
Ecole des Ponts ParisTech
Hydrology Meteorology and Complexity (HM&Co)
Hydrology Meteorology and Complexity (HM&Co)
France
Subject areas
Subject areas
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Olivier Talagrand
École Normale Supérieure
Géosciences, Laboratoire de Météorologie Dynamique
Géosciences, Laboratoire de Météorologie Dynamique
France
Subject areas
Subject areas
Predictability, probabilistic forecasts, data assimilation, inverse problems
Editors
Amit Apte
Indian Institute of Science Education and Research, Pune
Data Science
Data Science
India
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Rudy Calif
Université des Antilles
UFR SEN
Physics
UFR SEN
Physics
France
Subject areas
Subject areas
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Time series, machine learning, networks, stochastic processes, extreme events
Vincenzo Carbone
Università della Calabria
Dipartimento di Fisica
Dipartimento di Fisica
Italy
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Natale Alberto Carrassi
University of Bologna
Department of Physics
Department of Physics
Italy
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Predictability, probabilistic forecasts, data assimilation, inverse problems
Mickael D. Chekroun
Weizmann Institute of Science
Department of Earth and Planetary Sciences
Department of Earth and Planetary Sciences
Israel
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Time series, machine learning, networks, stochastic processes, extreme events
Time series, machine learning, networks, stochastic processes, extreme events
Jezabel Curbelo
Universitat Politècnica de Catalunya
Departament de Matemàtiques
Departament de Matemàtiques
Spain
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Reik Donner
Magdeburg-Stendal University of Applied Sciences
Water, Environment, Construction & Safety
Water, Environment, Construction & Safety
Germany
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Jinqiao Duan
Illinois Institute of Technology
College of Computing
Department of Applied Mathematics
College of Computing
Department of Applied Mathematics
United States
Subject areas
Subject areas
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Wansuo Duan
Institute of Atmospheric Physics, Chinese Academy of Sciences
LASG
LASG
China
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Jie Feng
Fudan
Department of Atmospheric and Oceanic Sciences
Department of Atmospheric and Oceanic Sciences
China
Subject areas
Subject areas
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Time series, machine learning, networks, stochastic processes, extreme events
Harindra Joseph Fernando
University of Notre Dame
Department of Civil Engineering and Geological Sciences
Department of Civil Engineering and Geological Sciences
United States
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Ulrike Feudel
University of Oldenburg
Institute for Chemistry and Biology of the Marine Environment
Institute for Chemistry and Biology of the Marine Environment
Germany
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Christian Franzke
Pusan National University
Center for Climate Physics, Institute for Basic Science
Climate System
Center for Climate Physics, Institute for Basic Science
Climate System
Korea, Republic Of
Subject areas
Subject areas
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Behzad Ghanbarian
Kansas State University
Geology Department
Geology Department
United States
Subject areas
Subject areas
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Richard Gloaguen
Helmholtz Institute Freiberg for Resource Technology
Exploration
Exploration
Germany
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Allen G. Hunt
Wright State University
Physics and Earth & Environmental Sciences
Physics and Earth & Environmental Sciences
United States
Subject areas
Subject areas
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Jürgen Kurths
Potsdam Institute for Climate Impact Research
Germany
Subject areas
Subject areas
Time series, machine learning, networks, stochastic processes, extreme events
Shaun Lovejoy
McGill University
Department of Physics
Department of Physics
Canada
Subject areas
Subject areas
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Ana M. Mancho
Consejo Superior de Investigaciones Científicas
ICMAT
ICMAT
Spain
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Norbert Marwan
Potsdam Institute for Climate Impact Research
Complexity Science
Complexity Science
Germany
Subject areas
Subject areas
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Time series, machine learning, networks, stochastic processes, extreme events
Takemasa Miyoshi
Japan
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Balasubramanya Nadiga
Los Alamos National Laboratory
United States
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
William I. Newman
University of California
Department of Earth and Space Sciences
Department of Earth and Space Sciences
United States
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Joachim Peinke
Carl-von-Ossietzky University Oldenburg
Institute of Physics and ForWind - Center for Wind Energy Research
Institute of Physics and ForWind - Center for Wind Energy Research
Germany
Subject areas
Subject areas
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Time series, machine learning, networks, stochastic processes, extreme events
Vicente Perez-Munuzuri
University of Santiago de Compostela
Physics
Physics
Spain
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Stefano Pierini
Università di Napoli Parthenope
Dipartimento di Scienze e Tecnologie
Dipartimento di Scienze e Tecnologie
Italy
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Kira Rehfeld
University of Tübingen
Geo- and Environmental Research Center
Department of Geoscience
Geo- and Environmental Research Center
Department of Geoscience
Germany
Subject areas
Subject areas
Time series, machine learning, networks, stochastic processes, extreme events
Juan Restrepo
Oak Ridge National Laboratory
Mathematics in Computation
Mathematics in Computation
United States
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Predictability, probabilistic forecasts, data assimilation, inverse problems
Irina I. Rypina
Woods Hole Oceanographic Institution
Physical Oceanography
Physical Oceanography
United States
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Adarsh Sankaran
APJ Abdul Kalam Technological University
TKM College of Engineering Kollam
Civil Engineering
TKM College of Engineering Kollam
Civil Engineering
India
Subject areas
Subject areas
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Time series, machine learning, networks, stochastic processes, extreme events
Victor Shrira
Keele University
School of Computing andf Mathematics
School of Computing andf Mathematics
United Kingdom
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Olivier Talagrand
École Normale Supérieure
Géosciences, Laboratoire de Météorologie Dynamique
Géosciences, Laboratoire de Météorologie Dynamique
France
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Pierre Tandeo
IMT Atlantique
Mathematical and Electrical Engineering Department
Mathematical and Electrical Engineering Department
France
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Ioulia Tchiguirinskaia
Ecole des Ponts ParisTech, HM&Co
France
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Luciano Telesca
National Research Council
Institute of Methodologies for Environmental Analysis
Institute of Methodologies for Environmental Analysis
Italy
Subject areas
Subject areas
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Time series, machine learning, networks, stochastic processes, extreme events
Zoltan Toth
NOAA Research
Global Systems Laboratory
Global Systems Laboratory
United States
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Bruce Tsurutani
Retired from Jet Propulsion Laboratory, Caltech
Space physics
Space physics
United States
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
Stéphane Vannitsem
Royal Meteorological Institute of Belgium
Meteorological and Climatological Information Service
Meteorological and Climatological Information Service
Belgium
Subject areas
Subject areas
Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events