PONDS - A Python Package for Generating Synthetic Datasets with Spatio-Temporal Shifts

Quantifying and comparing the performance of methods that detect abrupt changes in climate time series remains challenging due to limited ground-truth data and the complex, nonlinear and stochastic dynamics of the climate system. To address this gap, we present PONDS (Perturbed Observables in Noisy Dynamics Synthesiser), a new software package designed to generate synthetic, climate-like time series for benchmarking and methodological development. PONDS serves three core purposes: (1) mimicking real-world climate shift events through configurable perturbations applied to synthetic or observationally informed dynamical systems; (2) enabling evaluation of abrupt-shift detectors by providing standardized benchmark datasets with known structural and statistical properties; and (3) offering a flexible framework for incorporating alternative  time-series generators within a climate-data context.

PONDS provides a controlled environment for exploring the detectability of regime shifts under varying assumptions about noise characteristics and complexity of the shift events. This includes the generation of spatio-temporal clusters and time series of customizable configurations. For example, a user can generate shift cluster events that spatially overlap and shift event properties propagate. This bridging tool supports systematic sensitivity analysis and promotes reproducible comparison across detection algorithms.

PONDS aims to contribute to the session by offering a modular tool that is able to enhance data-driven abrupt shift detection tools and potential climate tipping points, by providing a benchmark oriented data synthesizer. It further helps to understand the various appearances of practically observed and theoretically expected shift events.