The #ElNiño is developing quickly
Check how past events impacted the area you are in. What were these?
- floods?
- droughts?
- storms?
Start preparing now as best you can
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Graphic source:
https://www.nytimes.com/2026/05/04/climate/el-nino-global-warming.html
Milyardan jet hareketlerini izleme sistemimiz, nükleer kriz ve küresel felaket senaryolarını erken tespit ediyor. 🚁🔍 #JetMonitoring #EarlyWarning
📌 ⚠️ Geofon info #japan #tsunami #earlywarning
#earthquake
#civilprotection
Source: GFZ Potsdam #germany APR-20
https://geofon.gfz.de/eqexplorer/?startdate=now-14days&enddate=now
This document presents a fully specified, pre-registered empirical validation pipeline for testing the CRTI (Compression–Response/Resonance Thermodynamic Index) framework on a canonical ecological regime-shift dataset: the Peter Lake whole-ecosystem manipulation experiment (Carpenter et al., 2011). The pipeline defines a reproducible workflow for constructing the state variables R (adaptive response capacity) and \Phi (structural compression) from multivariate time-series data, and for computing the composite index T = R/\Phi. All preprocessing steps, parameter choices, windowing strategies, and statistical tests are fixed ex ante and may not be modified post hoc. The design is explicitly falsification-first. Primary and secondary hypotheses, as well as detailed failure criteria, are pre-specified and reported with equal prominence to positive outcomes. The document does not claim empirical validation of the CRTI framework; it defines a transparent and reproducible protocol for testing whether T carries early-warning information prior to a documented regime shift. This pipeline provides a methodological foundation for fair comparison between CRTI-based metrics and classical early-warning signals under identical conditions. early warning signals, regime shifts, ecological data, Peter Lake, pre-registration, reproducibility, falsification, time series analysis, dynamical systems, complex adaptive systems, structural compression, adaptive capacity, Kendall tau, covariance analysis, CRTI, critical transitions
This paper introduces a structural class of two-dimensional competitive adaptive systems (CRTI-class systems) defined by competitive coupling, self-limitation, and a compact invariant domain. Using Poincaré index theory and the Stable Manifold Theorem, we show that systems in this class admitting two boundary attractors necessarily contain an interior saddle point whose stable manifold partitions state space into two qualitatively distinct basins corresponding to adaptive and compression-dominated outcomes. We further define a scalar index T = R/\Phi, where R denotes adaptive response capacity and \Phi denotes structural compression, and prove that T acts as a local first-order basin classifier in a neighbourhood of the saddle, without constituting a geometric distance to the separatrix. Operational estimators for R and \Phi are derived from linear response theory and spectral covariance analysis, enabling empirical application to multivariate time series. High-dimensional reduction, thermodynamic interpretation, and cross-domain universality are explicitly identified as open problems. The framework provides a mathematically grounded substrate for regime-shift analysis while maintaining clear limits of validity. bistability, basin of attraction, separatrix, competitive dynamical systems, regime shifts, early warning signals, complex adaptive systems, structural compression, adaptive capacity, Poincaré index, stable manifold, critical transitions, dynamical systems theory, CRTI
@patricksudlow That's true, and unfortunately, access to institutes in Iran from european servers is limited, making it difficult to retrieve scientific #data or access #earlywarning systems for #civildefence -
Therefore, I'm always waiting for messages from research colleagues who can offer redirects to the relevant websites
Currently, I have one in Cairo who is reviewing measurement data on nuclear fallout - Iran's news blackout, of course, also applies to research during the war -
This study examines whether structural-dynamical indicators provide earlier detection of fisheries collapse than classical reference-point metrics such as fishing pressure (F/F_MSY) and spawning stock biomass (SSB/B_MSY). While these traditional indicators achieve near-perfect classification performance in datasets with strong separation between collapsed and stable stocks, they encode system state rather than trajectory. Using 46 stock trajectories calibrated to the RAM Legacy Stock Assessment Database and focusing explicitly on the early-phase regime (10–20 years prior to collapse), we evaluate two dynamical indicators: the lag-1 autocorrelation of fishing pressure (X3) and the log-rate of change of relative spawning biomass (X5). Discrimination performance (AUC) is saturated across all models and therefore uninformative. Instead, we assess predictive value through lead-time analysis. We find that X3 and X5 detect impending collapse a mean of 7.6 and 9.0 years earlier, respectively, than classical reference-point indicators (p < 0.001). Operational deployment requires persistence filtering of at least four consecutive years to control false positives, under which X5 achieves a false-positive rate of 0.091 while retaining substantial lead-time advantage. These results indicate that dynamical indicators provide a complementary early-warning layer whose contribution is temporal rather than discriminative, shifting the evaluation of collapse prediction from classification performance to temporal detectability. The framework is not a replacement for classical fisheries metrics but an extension that captures trajectory-level information preceding threshold crossing. Keywords: early-warning signals, fisheries collapse, critical slowing down, lead-time detection, stock assessment, dynamical systems, regime shifts, ecological forecasting
Peter du Toit
S.v. N.Sönmez
Alcor News
AlcorNews
Susan Ville
Global News BC
Bernd von Mallinckrodt 🦋