| 29 Jan 2026
TOAD v1.0: A Python Framework for Detecting Abrupt Shifts and Coherent Spatial Domains in Earth-System Data
Abstract. Large-scale, nonlinear, abrupt, and potentially irreversible transitions in major Earth-system components are becoming increasingly likely under human pressures, with far-reaching consequences for ecosystems, climate stability, and human societies. Yet detecting and comparing such transitions across Earth System Model ensembles remains fragmented and inconsistent, hindering systematic assessment of tipping-point risks.
Here we present the first release of the Tipping and Other Abrupt events Detector (TOAD v1.0), an open-source, user-oriented Python framework for detecting abrupt changes in gridded Earth-system data. TOAD implements a modular three-stage pipeline consisting of 1) grid-level abrupt shift detection, 2) spatio-temporal clustering of co-occurring changes, and 3) consensus synthesis to identify statistically robust regions across ensemble members, variables, models, or methodological configurations and quantifies agreement in transition timing. The framework addresses key practical challenges of large-scale spatio-temporal clustering on geographic grids and provides diagnostic statistics and visualisation tools. Detection, clustering, and synthesis algorithms can be flexibly exchanged, supporting systematic method comparison and extensibility. TOAD functions as a data-introspection tool that reveals potentially tipping-relevant dynamics across spatial and temporal scales for subsequent, process-based analysis.
We apply TOAD to a synthetic benchmark, domain models of the Antarctic Ice Sheet and the global terrestrial biosphere, and a global Earth System Model ensemble of the North Atlantic Subpolar Gyre. Together, these demonstrations illustrate TOAD's applicability across diverse systems and establish a structured foundation for investigating where and when potentially tipping-relevant changes occur and for quantifying associated uncertainties, supporting coordinated assessment efforts such as the Tipping Points Modelling Intercomparison Project (TIPMIP).
The paper by Harteg et al “A Python framework for detecting abrupt shifts and coherent spatial domains in Earth-System data” offers a software package that combines previously published techniques. I have several concerns about this manuscript and its software.
Since there is no research novelty in the paper, I think it is more suitable for a software magazine, such as The Journal of Open Source Software
https://joss.theoj.org/
If the authors would like to target specifically geophysical community, the choice of the experiments should be different. There should be extensive demonstration of performance on artificial data, with clear explanation of the difference between a generic tipping and abrupt change points. In particular, abrupt changes cannot be detected using early warning indicators, which means that the proposed TOAD package should be compared with equivalent packages of change point detection, on the same datasets. See the R package by Killick
https://cran.r-project.org/web/packages/changepoint/index.html
package by James et al
https://cran.r-project.org/web/packages/ecp/index.html
spectral change point package in Python
https://github.com/Lucew/changepoynt
and others.
A new software is a welcome addition when it performs the same or better compared with already existing packages, and the paper should demonstrate this on the same datasets.
The authors consider several modelled datasets. Can they add a 2D change point of an observed dataset? In particular, it would be interesting to know how the technique performs on observed short datasets that are currently available.
The blocks “how technique works” should be prepared in the format of pseudocode.
Section 3.2 should be called “Antarctic ice sheet model data”. In this section, it is not clear how model years were generated. Similarly, amend the title of section 3.3 to state it clearly that it is a modelled example.
In section 3.3., the number of parameters is rather large, and like this tuning detection of changes may become rather arbitrary – what tool can be introduced for optimised choice of parameter values?
It the caption of figure 5, the authors mention that only negative shifts were considered. If positive shifts are excluded, it would be interesting to know the number of those – how the model performs in both direction is indicative of its accuracy.
I did not attempt to install and run the package due to the lack of time, but I had a look at the GitHub. I note that there are other TOAD packages on GitHub (and this toad package is not easy to find, as it is not indexed by Google):
https://github.com/batrachianai/toad
https://github.com/gianwario/TOAD
https://github.com/amphibian-dev/toad
I am not sure if the acronym is good, especially as it misses the keyword “event”. Reconsider the acronym?