The NPG paper of the month for January 2023 was awarded to “Brief communication: Climate science as a social process – history, climatic determinism, Mertonian norms and post-normality” by Hans von Storch. One could argue that climate science is part of geophysics, or more precisely that part of climate science is part of geophysics. But when referring to ”processes” in ”Nonlinear Processes in Geo ...[Read More]
Climate Tipping Points through the “eyes” of the Masters: a virtual art exhibition
We are running out of time in fighting the effects of climate change. With the greenhouse gasses emissions steady, the planet is reaching warming levels that could cause some large ecosystems to tip in different states. A tipping point in the climate system refers to a threshold beyond which a small change in a particular variable can cause a significant and potentially irreversible change in the ...[Read More]
NP Interviews: Davide Faranda (CNRS) on the topic “global warming and extreme events in Europe”
Davide Faranda is a physicist at the French National Center for Scientific Research (CNRS). His main interests are devoted to the construction of a statistical mechanical and dynamical systems framework for the study of atmospheric motions. In particular, his works aim at the characterization of the metastable states of the atmospheric circulation with simple mathematical and statistical tools, an ...[Read More]
NPG Paper of the Month: “Inferring the instability of a dynamical system from the skill of data assimilation exercises”
Scatter plots of the largest Lyapunov exponent (left) and Kolmogorov-Sinai entropy (right) against the skill of DA measured as the RMSE of the analysis. The experiments are done with model configurations displaying fully developed chaos (main panels) and with weakly chaotic configurations (insets). In all cases a strong observational constraint is applied: we measure the entire state vector at each time step. The theoretical error upper bound are also displayed (red markers).
The NPG paper of the month was awarded to “Inferring the instability of a dynamical system from the skill of data assimilation exercises” by Yumeng Chen, Alberto Carrassi, and Valerio Lucarini. Geophysical systems are usually described by a set of dynamical equations that are often non-linear and chaotic (Ghil and Lucarini, 2020). Errors about the initial state can grow, shrink, or sta ...[Read More]