Determining whether the Atlantic Meridional Overturning Circulation (AMOC)'s transport is in decline is challenging due to the short duration of continuous observations. To estimate how many years are needed to detect a decline, we conduct a simulation study using synthetic data that mimics an AMOC time series. The time series' characteristics are reproduced using the trend, variance, and autocorrelation coefficient of the AMOC strength at 26.5°N from 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models under the RCP8.5 future scenario, and from RAPID observations (2004–2018). Our results suggest that the 14‐year RAPID length has just entered the lower limits of the trend's “detection window” based on synthetic data generated using CMIP5 trends and variability (14–42 years; median 24 years), but twice the length is required for detectability based on RAPID variability (29–67 years; median 43 years). The annual RAPID trend is currently not statistically significant (−0.11 Sv yr⁻¹, p > 0.05).

We apply tipping point analysis to nine observational oxygen concentration records around the globe, analyse their dynamics and perform projections under possible future scenarios, leading to oxygen deficiency in the atmosphere. The analysis is based on statistical physics framework with stochastic modelling, where we represent the observed data as a composition of deterministic and stochastic components estimated from the observed data using Bayesian and wavelet techniques.

We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAPE ≈ 1.6% and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions.