Theory-informed and Data-driven Approaches to Advance Climate Sciences - Now Online

Read these papers now View in Browser OUR JOURNALS MANAGE MY ALERTS ABOUT AIP PUBLISHING Chaos Special Topic: Theory-informed and Data-driven Approaches to Advance Climate Sciences ALL ARTICLES > The understanding of the fundamental dynamical and statistical properties of the climate system is a grand scientific challenge of contemporary science. Despite substantial progress, reaching a complete theory able to relate climate variability and climate response across scales is proving elusive. While a lot of progress has been made, various fundamental theoretical and practical problems still need to be addressed. In the context of numerical weather forecast, accurate and efficient parametrizations are key to achieving high skill in prediction. We are now living in the time when machine learning is revolutionizing the way we look at complex systems, in terms of both modelling and data processing. A critical appraisal of its potential and pitfalls in the context of climate-related research is extremely timely. The goal of this special issue is to collect contributions that, using theory-informed and/or data-driven methods, aim at advancing: i) our understanding of the dynamical properties of the climate system and of its individual components, ii) our ability to model the climate system and of its individual components and to achieve accurate predictions at different spatial and temporal scales; iii) our ability to merge optimally data and model outputs using innovative data assimilation methods across scales and/or involving one or more components of the climate systems. Guest Editors: Valerio Lucarini, Davide Faranda, Alexander Feigin, Juan Restrepo, and Nikki Vercauteren FAST TRACK  Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh READ MORE > REGULAR ARTICLES  Dynamical footprints of hurricanes in the tropical dynamics D. Faranda, G. Messori, P. Yiou, S. Thao, et al. READ MORE > A complex network approach to study the extreme precipitation patterns in a river basin Ankit Agarwal, Ravi Kumar Guntu, Abhirup Banerjee, Mayuri Ashokrao Gadhawe, et al. READ MORE > Study of interaction and complete merging of binary cyclones using complex networks Somnath De, Shraddha Gupta, Vishnu R. Unni, Rewanth Ravindran, et al. READ MORE > Data-driven stochastic model for cross-interacting processes with different time scales A. Gavrilov, E. Loskutov, A. Feigin READ MORE > Systematic calculation of finite-time mixed singular vectors and characterization of error growth for persistent coherent atmospheric disturbances over Eurasia Courtney Quinn, Terence J. O'Kane, Dylan Harries READ MORE > Asymptotic behavior of the forecast–assimilation process with unstable dynamics Dan Crisan, Michael Ghil READ MORE > Scale dependence of fractal dimension in deterministic and stochastic Lorenz-63 systems T. Alberti, D. Faranda, V. Lucarini, R. V. Donner, et al. READ MORE > The deterministic excitation paradigm and the late Pleistocene glacial terminations Stefano Pierini READ MORE > Decomposing the dynamics of the Lorenz 1963 model using unstable periodic orbits: Averages, transitions, and quasi-invariant sets Chiara Cecilia Maiocchi, Valerio Lucarini, Andrey Gritsun READ MORE > Data driven adaptive Gaussian mixture model for solving Fokker–Planck equation Wenqing Sun, Jinqian Feng, Jin Su, Yunyun Liang READ MORE > The climatic interdependence of extreme-rainfall events around the globe Zhen Su, Henning Meyerhenke, Jürgen Kurths READ MORE > GPU-based, interactive exploration of large spatiotemporal climate networks Stefan Buschmann, Peter Hoffmann, Ankit Agarwal, Norbert Marwan, et al. READ MORE > An improved framework for the dynamic likelihood filtering approach to data assimilation Dallas Foster, Juan M. Restrepo READ MORE > Conditional Gaussian nonlinear system: A fast preconditioner and a cheap surrogate model for complex nonlinear systems Nan Chen, Yingda Li, Honghu Liu READ MORE > Contributions to surface air temperature trends estimated from climate time series: Medium-term causalities Igor I. Mokhov, Dmitry A. Smirnov READ MORE > Memory-based parameterization with differentiable solver: Application to Lorenz '96 Mohamed Aziz Bhouri, Pierre Gentine READ MORE > Systematic multi-scale decomposition of ocean variability using machine learning Christian L. E. Franzke, Federica Gugole, Stephan Juricke READ MORE > Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structure–function coupling Leila Constanza Hernandez Rodriguez, Praveen Kumar READ MORE > Causal interaction in high frequency turbulence at the biosphere–atmosphere interface: Structural behavior Leila Constanza Hernandez Rodriguez, Praveen Kumar READ MORE > Stochastic paleoclimatology: Modeling the EPICA ice core climate records N. D. B. Keyes, L. T. Giorgini, J. S. Wettlaufer READ MORE > Physics captured by data-based methods in El Niño prediction G. Lancia, I. J. Goede, C. Spitoni, H. Dijkstra READ MORE > Random templex encodes topological tipping points in noise-driven chaotic dynamics Gisela D. Charó, Michael Ghil, Denisse Sciamarella READ MORE > Dynamical stability indicator based on autoregressive moving-average models: Critical transitions and the Atlantic meridional overturning circulation Marie Rodal, Sebastian Krumscheid, Gaurav Madan, Joseph Henry LaCasce, et al. READ MORE > Guidelines for data-driven approaches to study transitions in multiscale systems: The case of Lyapunov vectors Akim Viennet, Nikki Vercauteren, Maximilian Engel, Davide Faranda READ MORE > Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl, Kira Rehfeld READ MORE > Forced signal and predictability in a prototype climate model: Implications for fingerprinting based detection in the presence of multidecadal natural variability S. Kravtsov, A. Gavrilov, M. Buyanova, E. Loskutov, et al. 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