the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Jul 2026
A low-dimensional dynamical systems approach for ensemble design and interpretation in climate science
Abstract. The design and use of conceptual models have been a longstanding and successful practice of mathematicians and scientists to study and gain insights about complex phenomena in climate and beyond. Here, we demonstrate how low-dimensional dynamical systems can also be useful in the design and interpretation of climate model ensembles. We argue that, provided they possess a small number of key characteristics, such systems can serve as computationally inexpensive laboratories for investigating questions of ensemble methodology that would be difficult to explore systematically in comprehensive Earth System Models. To this end, we identify the characteristics required of informative low-dimensional systems, formalise a framework for their use, and illustrate the approach through a series of examples.
- Preprint
(764 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2026-4138', Anonymous Referee #1, 25 Aug 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-4138/egusphere-2026-4138-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2026-4138-RC1 -
RC2: 'Comment on egusphere-2026-4138', Anonymous Referee #2, 20 Sep 2026
This manuscript presents a new conceptual framework for using low-dimensional dynamical systems as computational tools for the design and interpretation of climate model ensembles. The authors argue that low-dimensional systems possessing a small set of key characteristics shared with climate models (complexity, multiscale behaviour, multiple components, nonlinearity/chaos, and non-autonomy) can provide valuable insights into ensemble methodology that would otherwise be difficult to obtain using computationally expensive ESMs. The framework is illustrated through a series of examples drawn from previous studies of initial condition uncertainty, parametric uncertainty, and ensemble experimentation tools.
Overall, I found this to be a thoughtful, well-written, and timely contribution. Climate science increasingly relies on ensembles, while at the same time the costs of comprehensive climate model experiments continue to grow. The manuscript addresses an important methodological gap by proposing a clear rationale for investigating ensemble design questions in simplified systems before committing substantial computational resources to ESM experiments. The paper is accessible, logically structured, and succeeds in articulating a compelling vision for an expanded role of conceptual models in climate science.
The principal strength of the paper is its original perspective. Low-dimensional dynamical systems have long been used to understand climate dynamics, predictability, and nonlinear behaviour, but the authors convincingly argue for an additional role: understanding ensemble methodologies themselves rather than the climate system directly. This distinction is clearly articulated and provides a useful conceptual contribution that is likely to resonate with both dynamical systems researchers and climate modellers.
A second strength is the clear identification of the key characteristics that make climate-model ensembles challenging to design and interpret. The discussion of complexity, multiscale dynamics, coupled components, chaos, and non-autonomous forcing provides a useful organizing framework for assessing whether a low-dimensional system is suitable for a particular methodological question.
Rather than remaining purely conceptual, the manuscript demonstrates how the proposed framework has already been applied in practice to questions of initialization, uncertainty characterization, and ensemble construction. These examples help convince the reader that the approach is not merely philosophical but can provide practical guidance for climate-model experimentation.
I have only a few suggestions that could further strengthen the paper:
- Clarify the limits of transferability. While the manuscript appropriately emphasizes that low-dimensional systems should not be interpreted as quantitative surrogates for ESMs, the discussion could benefit from a slightly more explicit account of how researchers might assess whether a lesson learned in a conceptual model is likely to transfer to a high-dimensional climate model. A few concrete criteria or examples would help readers apply the framework.
- Strengthen the connection to contemporary large-ensemble practice. The paper discusses ensemble design broadly, but a brief discussion of how the proposed framework could specifically inform modern large-ensemble initiatives, perturbed-parameter ensembles, or km-scale modelling efforts would increase its relevance for a wider climate-modelling audience.
- Links with weather forecasting. The authors may wish to briefly discuss whether ensemble design techniques developed in probabilistic weather forecasting can inform climate ensemble design. Although weather and climate predictions are affected by different uncertainty sources and timescales, both face the challenge of constructing informative ensembles. A short discussion of which methods or principles may transfer, and how low-dimensional dynamical systems could help evaluate their applicability to climate projections, would broaden the paper's methodological relevance.
In summary, this paper makes a valuable methodological contribution by formalizing a framework through which low-dimensional dynamical systems can be used to investigate ensemble design and interpretation. The ideas are thoughtful, well-motivated, and clearly communicated. I believe the manuscript will be of interest to researchers working in climate modelling, uncertainty quantification, ensemble prediction, and nonlinear dynamical systems.
Citation: https://doi.org/10.5194/egusphere-2026-4138-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 197 | 63 | 24 | 284 | 20 | 21 |
- HTML: 197
- PDF: 63
- XML: 24
- Total: 284
- BibTeX: 20
- EndNote: 21
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
