Alternate states and intermingledness in complex high-dimensional systems

George Datseris; Jacob Haqq-Misra; Johannes Lohmann; Ois\'in Hamilton

REVIEW 2 major objections 6 minor 13 references

Alternate states and intermingledness in complex high-dimensional systems

T0 review · 2 major / 6 minor · reviewed 2026-07-13 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A featurize-and-group workflow decides which alternate states exist in high-dimensional simulations and which observables best separate them.

desk verdict Usable open workflow that recovers expert multistability on three climate ensembles and ranks which observables actually separate the states; soft spot is the ad-hoc Q objective, not the recovery results. read the letter →

arxiv 2604.09661 v2 pith:UPKN6IJI submitted 2026-03-30 physics.ao-ph nlin.AOphysics.data-anstat.ME

George Datseris , Johannes Lohmann , Oisín Hamilton , Jacob Haqq-Misra This is my paper · ORCID

classification physics.ao-phnlin.AOphysics.data-anstat.ME

keywords multistabilityalternatestateshigh-dimensionaldataDBSCANclusteringinterminglednessclimatetippingbasinsofattractionfeatureselection

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Complex systems such as climate models, power grids, and biological networks often admit multiple alternative long-term states, yet high-dimensional finite ensembles are hard to classify without subjective judgment. This paper supplies an algorithmic workflow that extracts statistical features from diagnostic timeseries, clusters them with an iterative advanced density-based method, and optimizes over feature subsets so that the number of clearly distinguishable states and the observables that best separate them are chosen together. Once the states are found, a new indicator called intermingledness measures how mixed the states (or their basins of attraction) are along each diagnostic, revealing which variables are useful for monitoring or early warning and which are not. The method recovers expert-known attractors on three climate ensembles—Atlantic overturning, mid-latitude ocean–atmosphere flow, and exoplanet habitability—and is released as open-source code that reads ordinary NetCDF files.

What carries the argument

Intermingledness: for each group and each one-dimensional diagnostic, the ratio of mean intra-group pairwise distance to mean inter-group pairwise distance; values near 1 mean the groups cannot be told apart along that diagnostic.

What would settle it

On a held-out ensemble whose attractors are already known by exhaustive long control runs, re-run the full optimization with the same candidate features and check whether the recovered partition and the top-ranked diagnostics match the known classification.

Watch

Extended reading notes

Core claim

Finite high-dimensional simulation ensembles can be partitioned into objectively distinguishable alternate states by projecting trajectories onto diagnostic features, clustering those features with iterative advanced DBSCAN, and ranking candidate feature subspaces by a grouping-quality score that rewards more states, fewer dimensions, and fewer outliers; the same partition yields an intermingledness matrix that quantifies, per diagnostic, how similar or mixed the states and their basins are.

Load-bearing premise

The ad-hoc grouping-quality score that multiplies silhouette mean by powers of the number of states, features, and outliers is assumed to select the scientifically correct feature subspaces; if that score mis-ranks, the claimed optimal observables change.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is a methods paper that does what it claims on the data it has. The authors take their earlier featurize-and-group / ADBSCAN work, add iterative re-clustering, a combinatorial search over feature subspaces, and a new sparse-data intermingledness measure, then show that the pipeline recovers the expert-known attractor counts on Veros AMOC and MAOOAM mid-latitude flow, finds physically sensible best features (North Atlantic surface/subsurface temperature with AMOC strength; particular streamfunction modes), and reproduces a known destabilization under emissivity continuation. On the exoplanet habitability set they skip clustering and just map intermingledness onto pre-labeled classes, which is still informative. Open code and NetCDF-friendly tooling are shipped; that matters for TipMIP-style work and for anyone sitting on expensive ensembles who currently decides attractors by eye.

What is new is the full sparse-data workflow plus intermingledness (and a boundary variant) that can be computed per diagnostic and per group without dense basin covering. The ranking of which observables best and least separate states and basins is the practical payoff, and the three applications make that concrete rather than abstract.

The soft spot is exactly the one the reader flagged: grouping quality Q is empirical, weights are free parameters, and silhouette mean alone does not prefer more clusters. Appendix B shows robustness over a range of weights and the recovery experiments still match expert labels, so Q is not load-bearing for the central claim, but anyone applying this will need to re-run the sensitivity. Absolute intermingledness values are also hard to interpret without sampling context; the paper is honest about that. Simulation count and transient handling remain practical limits, again stated.

Math and citations look solid; self-cites to DLW23/DW22 are the base algorithms, not circular. This is for people who run or re-analyze high-dimensional multistable ensembles (climate, power grids, networks) and want an objective, reproducible first pass. I would send it to referees; it is a usable advance with clear limitations, not a theorem of uniqueness.

Referee Report

2 major / 6 minor

Summary. The paper proposes a computational workflow for identifying and analysing alternate states in finite, high-dimensional simulation ensembles. Building on a featurize-and-group approach with an iterative advanced DBSCAN (IA-DBSCAN), it optimizes over feature subspaces using an empirically defined grouping quality Q (Eq. 1) to decide whether clearly distinguishable alternate states exist and which observables best separate them. It further introduces intermingledness, a per-group, per-dimension ratio of pairwise-averaged intra- to inter-group distances, to quantify similarity of attractor features and of basins of attraction. The method is demonstrated on three climate ensembles (Veros AMOC multistability, MAOOAM midlatitude flow with continuation in emissivity, and ExoPlaSim exoplanet habitability classes), recovers expert-known groupings where available, and is released with open-source code.

Significance. If the workflow performs as claimed, it fills a genuine methodological gap: objective, transferable analysis of multistability and alternate operating regimes in sparse, high-dimensional climate and complex-system data where traditional dense basin methods and simple clustering fail. Recovering expert attractor counts on Veros and MAOOAM, reproducing a known destabilization under continuation, and providing open code and an intermingledness implementation in DynamicalSystems.jl are concrete strengths. The indicator and feature-ranking outputs could usefully guide monitoring, early-warning design, and model intercomparison (e.g. TipMIP, habitability MIPs). The contribution is primarily methodological rather than a uniqueness theorem; its value rests on practical reliability and reproducibility, which the manuscript largely supports.

major comments (2)

Eq. (1) and Appendix B: Grouping quality Q is acknowledged as empirically derived with no theoretical foundation, and silhouette mean S is shown to be nearly independent of cluster count A and feature count F. Fig. 7 further shows that weight choices can change the recovered number of attractors (e.g. 4 vs 5 when iteration is off). Because the central claim is that the workflow algorithmically decides which alternate states exist and which observables are optimal, the manuscript needs clearer default weight recommendations, a short protocol for sensitivity checks practitioners must run, and explicit discussion of failure modes when Q mis-ranks feature sets. Without that, the claimed objectivity of the recovered states remains partly practitioner-dependent.
Sections 3.1–3.3 and 4.2: Validation is performed only on ensembles already known (or pre-labeled) to contain alternate states. The workflow’s claim to decide whether alternate states are “clearly distinguishable, if any” therefore lacks a demonstrated null or false-positive case (e.g. a monostable ensemble or pure noise features). A brief controlled test or synthetic monostable example would substantially strengthen confidence that the pipeline does not invent structure when none exists, which is load-bearing for applications where the answer is unknown a priori.

minor comments (6)

Notation for Q in Eq. (1) uses A, F, O without fully consistent symbols in the surrounding text (sometimes n_outliers, etc.); unify symbols and define n_min explicitly in the main text, not only in Appendix B.
Figure 1 step labels and spine highlighting are helpful but dense; a short legend or caption sentence listing what each panel’s axes represent would improve readability for non-specialists.
Section 2.2: The list of possible features is good, but the paper only uses late-time mean/std. A one-sentence justification why more complex features (entropy, spectral) were unnecessary for these three datasets would help transferability claims.
Appendix C: Boundary intermingledness is interesting but under-used in the main applications. Either apply it once to Veros/MAOOAM basins or move the full comparison to a shorter note so the main text stays focused.
Typos and style: “posses” → “possess” (abstract); “intermingledess” in §6; occasional missing articles; “MOAOOAM” in Fig. 4 caption should be MAOOAM.
Data availability: MAOOAM trajectories “available upon request” weakens reproducibility relative to Veros and ExoPlaSim; if possible, deposit a reduced feature table or a subset of trajectories.

Circularity Check

1 steps flagged · score 1.0 of 10

No significant circularity: the workflow is an empirical clustering pipeline validated against independent expert classifications; Q and intermingledness are new constructs, not tautological restatements of their inputs.

self citation load bearing [§2.3 Feature grouping; also Intro and §2.7]
"In Ref. [DLW23] we have substantially improved the algorithm into the version we called ADBSCAN (A for Advanced) where the parameter ε is optimized to yield a clustering of optimal quality. ... In this work we further augmented the algorithm to IA-DBSCAN (I for Iterative)..."

The core clustering engine (ADBSCAN) is taken from the authors' prior paper DLW23. This is ordinary methods reuse, not a circular derivation: the present paper adds iteration, the new quality metric Q, feature-subspace search, and intermingledness, then validates on independent ensembles. The self-citation does not force the recovered attractors or the intermingledness values; those are outputs of applying the pipeline to new data. Flagged only as minor self-citation of the base algorithm.

full rationale

The paper's central claim is a computational workflow (featurize → IA-DBSCAN → optimize grouping quality Q → intermingledness) that recovers alternate states from finite high-dimensional ensembles. Validation is external: on three climate datasets the recovered groups match prior expert hand-classifications (Veros AMOC attractors, MAOOAM LFV attractors, SAMOSA habitability classes). The base clustering algorithm is imported from DLW23/DW22 (self-citations by overlapping authors), but those citations supply a reusable density-based procedure, not a uniqueness theorem that forces the climate results; the new pieces (iterative re-application, the ad-hoc Q of Eq. 1, and the intermingledness ratio) are defined and applied here. Appendix B explicitly states that Q has no theoretical foundation and is empirically motivated; sensitivity to its weights is shown, so Q is not presented as a first-principles derivation. Intermingledness is a ratio of pairwise intra- to inter-group distances and is not claimed to equal any fitted parameter. Continuation matching likewise uses a simple centroid-distance rule. No equation reduces a claimed prediction to a fitted input by construction, and no uniqueness result is smuggled in to forbid alternatives. The single minor self-citation of the base algorithm is not load-bearing for the climate recoveries or for the definition of intermingledness. Score 1 reflects that ordinary self-citation of prior methods work without circular reduction of the paper's own claims.

Assumptions & free parameters 4 free parameters · 4 assumptions · 3 invented entities

The claim rests on standard clustering practice plus several paper-specific design choices: an empirical quality score with free weights, the premise that alternate states are feature-separable in a low-d projection, and a newly defined intermingledness ratio. No new physical entities are postulated; the invented objects are algorithmic/metric constructs whose independent evidence is the three case studies and comparison to basin entropy on toy basins.

free parameters (4)

Grouping-quality weights w_A, w_F, w_O
Eq. (1) multiplies silhouette mean by powers of attractor count, feature count, and outlier count; weights are practitioner-chosen. Appendix B shows broad plateaus but no unique optimum; output can change with weights.
Feature-subspace size d and combinatorial subsample
Clustering is restricted to d randomly/combinatorially chosen features (authors suggest small d ~4–6); when combinations explode they subsample, which can miss the true best set.
Minimum points per group n_min and outlier handling
Q rewards more groups only if each has at least n_min members; this threshold is not derived from the data-generating process.
Choice of feature functionals (late-time mean/std, fraction of series kept)
Demonstrations use mean or std of a final portion (e.g. last 1000 yr or last 25%); different functionals can change clusters, especially with slow transients.

assumptions (4)

ad hoc to paper If alternate states are uniquely distinguishable, there exists at least one feature on which they form separable clusters; absence of such a feature means the dataset has no clearly distinguishable alternate states.
Stated as the central assumption in §2.2; defines success of the method by construction of feature separability.
domain assumption Density-based clustering (ADBSCAN/IA-DBSCAN) with silhouette-optimized radius can recover the correct number of groups without a preset k in sparse feature space.
Inherited from DLW23 and used throughout Steps 4–5; known failure modes under extreme sparsity are acknowledged in §4.2.
ad hoc to paper Pairwise-averaged intra/inter group distance ratios on 1-D projections meaningfully quantify separation of attractors and basins for heterotypic diagnostics.
Definition of intermingledness in §2.6; authors note absolute values are hard to interpret and can mislead for nested or undersampled basins (Appendix C).
domain assumption Late-time statistics of projected diagnostic variables are sufficient proxies for high-dimensional attractors when full re-simulation is impossible.
Data-generation and feature-extraction steps (§2.1–2.2); standard in climate ensemble analysis but can confuse long transients with attractors.

invented entities (3)

Intermingledness I_i (and boundary intermingledness)
purpose: Per-group, per-dimension score of how mixed alternate-state features or basins are, for sparse projected data where basin entropy is inapplicable.
Newly defined in §2.6 and Appendix C; independent evidence is limited to toy basin comparisons and the three applications, not external experimental confirmation.
Grouping quality Q
purpose: Scalar objective that prefers more attractors, fewer features, and fewer outliers when selecting feature subspaces.
Eq. (1); authors state it is empirical with no theoretical foundation (Appendix B).
IA-DBSCAN (iterative advanced DBSCAN)
purpose: Optionally re-cluster each found cluster to resolve subtle multistability (used for Veros).
Augmentation of ADBSCAN from DLW23; utility shown on one dataset.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Alternate states and intermingledness in complex high-dimensional systems." pith.science (2026). https://pith.science/paper/UPKN6IJI

@misc{pith2026260409661,
  author       = {Pith},
  title        = {Pith review of: Alternate states and intermingledness in complex high-dimensional systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UPKN6IJI}},
  note         = {Machine review of arXiv:2604.09661}
}

read the original abstract

Many natural systems posses, and can transition between, multiple alternative states. For example, a climate ``tipping element'' is a climate component that can transition to an alternative steady state due to an external perturbation such as global warming. Despite the potential impact, existence of alternate states in realistic, complex simulations (e.g. climate models) remain poorly understood. Arguably a reason for this is the lack of applicable methodology that explicitly targets finite yet high-dimensional datasets. In this work we utilize recent progress in computational nonlinear dynamics to formulate a workflow that analyses potentially multi-state simulation data and decides algorithmically what are the alternate states contained within, if any are clearly distinguishable. The framework undergoes an optimization routine that showcases which observables in the data best differentiate the alternate states, and which ones do not differentiate at all, which could be used to guide monitoring and early-warning for multistable components in climate or ecosystems. Finally, once the alternate states have been found, we define an indicator called ``intermingledness''. It quantifies differences and similarities between alternate states, as well as for their basins of attraction (if applicable), across various diagnostic variables. We analyse and present results using three diverse climate datasets: Atlantic ocean circulation, atmospheric midlatitude flow, and habitability of exoplanets. The method is not exclusive to climatic data, but applicable to a variety of cases, including complex networks such as power grids or biological networks. We also provide easy-to-use open source code for applying the workflow to new data.

Figures

Figures reproduced from arXiv: 2604.09661 by the authors.

**Figure 2.** Analysis of Veros data. The abbreviations of the diagnostic variables (’sst [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** As in Fig [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Continuation, as defined in §2.7, for the MOAOOAM dataset. Top panel: the centroids for each feature group (attractor) for varying the atmospheric emissivity ε, projected to the diagnostic variable psi a 3. This atmospheric mode corresponds to the barotropic streamfun…

**Figure 5.** Figure 5: Analysis of data produced by the ExoPlaSim model for the habitability of an exoplanet [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: Reported silhouette mean s (quantity optimized to yield optimal DBSCAN clustering) during DBSCAN clustering for the Veros data of §3.1, versus number of groups created in the clustering. The marker shape corresponds to the number of features used, and markers are plott…

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 1 linked inside Pith

[1]

Stabilizing feed- backs allow for multiple states of the Greenland Ice Sheet in a fully coupled Earth System – Ice Sheet Model

[AKM26] Malena Andernach, Marie-Luise Kapsch, and Uwe Mikolajewicz. “Stabilizing feed- backs allow for multiple states of the Greenland Ice Sheet in a fully coupled Earth System – Ice Sheet Model”. In:The Cryosphere20.2 (Feb. 2026), pp. 1047–1069.��: 1994-0424.��:��.��:�� . [And11] Theodore W...

2026
[2]

Two decades of climate tipping points research: Progress and outlook

[Arm24] David I. Armstrong McKay. “Two decades of climate tipping points research: Progress and outlook”. In:Dialogues on Climate Change1.1 (Dec. 2024), pp. 30–38.��: 2976- 8659, 2976-8659.��:��. [BKV19] M. Brunetti, J. Kasparian, and C. V´ erard. “Co-existing climate attractors in a cou- pled aquaplanet”. In:Climate Dynamics53.9...

arXiv 2024
[3]

Estimating fractal dimensions: A comparative review and open source implementations

[Dat+23] George Datseris et al. “Estimating fractal dimensions: A comparative review and open source implementations”. In:Chaos: An Interdisciplinary Journal of Nonlinear Science33.10 (Oct. 2023).��: 1089-7682.��:��.��:�� . [Dat18] George Datseris. “DynamicalSystems.jl: A Julia software library for chaos...

2023
[4]

The Modular Arbitrary- Order Ocean-Atmosphere Model: MAOOAM v1.0

��:��. (Visited on 05/27/2025). [DDV16] Lesley De Cruz, Jonathan Demaeyer, and St´ ephane Vannitsem. “The Modular Arbitrary- Order Ocean-Atmosphere Model: MAOOAM v1.0”. In:Geoscientific Model Develop- ment9.8 (Aug. 2016), pp. 2793–2808.��: 1991-9603.��:�� . (Visited on 04/07/2025). [Dei+23] Russell Deitrick et...

2025
[5]

ComplexityMeasures.jl: Scalable soft- ware to unify and accelerate entropy and complexity timeseries analysis

[DH25] George Datseris and Kristian Agasøster Haaga. “ComplexityMeasures.jl: Scalable soft- ware to unify and accelerate entropy and complexity timeseries analysis”. In:PLOS ONE20.6 JUNE (2025), pp. 1–21.��: 19326203.��:�� .��:��. [DLW23] George Datseris, Kalel Luiz Rossi, and Alexand...

2025
[6]

Alternative signaling pathways: when, where and why?

[GZ05] Yunchen Gong and Zhaolei Zhang. “Alternative signaling pathways: when, where and why?” en. In:FEBS Lett.579.24 (Oct. 2005), pp. 5265–5274. [Ham+23] Ois´ ın Hamilton et al. “Multistability in a coupled ocean–atmosphere reduced-order model: Nonlinear temperature equations”. In:Quarterly Journal of the Royal Meteo- rological Society149.757 (Sept. 2023...

2005
[7]

Demarcating circulation regimes of synchronously rotating terrestrial planets within the habitable zone

18 [Haq+18] Jacob Haqq-Misra et al. “Demarcating circulation regimes of synchronously rotating terrestrial planets within the habitable zone”. In:The Astrophysical Journal852.2 (2018), p

2018
[8]

The Sparse Atmospheric Model Sampling Analysis (SAMOSA) Intercomparison: Motivations and Protocol Version 1.0: A CUISINES Model Intercom- parison Project

[Haq+22] Jacob Haqq-Misra et al. “The Sparse Atmospheric Model Sampling Analysis (SAMOSA) Intercomparison: Motivations and Protocol Version 1.0: A CUISINES Model Intercom- parison Project”. In:The Planetary Science Journal3.11 (Nov. 2022), p. 260.��: 2632-3338.��:��.��:�� . [Har+24] Carsten Hartmann et...

2022

Show all 13 references

[9]

The snowball Earth transition in a climate model with drifting parameters: Splitting of the snapshot attractor

[KHH19] B´ alint Kasz´ as, T´ ımea Haszpra, and M´ aty´ as Herein. “The snowball Earth transition in a climate model with drifting parameters: Splitting of the snapshot attractor”. en. In:Chaos29.11 (Nov. 2019), p. 113102. [Kim+18] Heetae Kim et al. “Multistability and variati...

2019
[10]

Risk of tipping the overturning circula- tion due to increasing rates of ice melt

[LD21] Johannes Lohmann and Peter D. Ditlevsen. “Risk of tipping the overturning circula- tion due to increasing rates of ice melt”. In:Proceedings of the National Academy of Sciences of the United States of America118.9 (2021), pp. 1–6.��: 10916490.��: ��...

2021
[11]

Robustness of Competing Climatic States

[Rag+22] Charline Ragon et al. “Robustness of Competing Climatic States”. In:Journal of Climate35.9 (2022), pp. 2769–2784.��: 0894-8755.��:�� . [Rah95] S. Rahmstorf. “Multiple Convection Patterns and Thermohaline Flow in an Idealized OGCM”. In:J. Cli...

2022
[12]

Multistability and Rare Spon- taneous Transitions in Barotropic beta-Plane Turbulence

[SRB21] Eric Simonnet, Joran Rolland, and Freddy Bouchet. “Multistability and Rare Spon- taneous Transitions in Barotropic beta-Plane Turbulence”. In:Journal of the Atmo- spheric Sciences78 (6 June 2021), pp. 1889–1911.��: 0022-4928.��:�� . [TD26] J. Th...

2021
[13]

Atmospheric regimes and trends on exoplanets and brown dwarfs

[Zha20] Xi Zhang. “Atmospheric regimes and trends on exoplanets and brown dwarfs”. In: Research in Astronomy and Astrophysics20.7 (2020), p

2020

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.

[1] [1]

Stabilizing feed- backs allow for multiple states of the Greenland Ice Sheet in a fully coupled Earth System – Ice Sheet Model

[AKM26] Malena Andernach, Marie-Luise Kapsch, and Uwe Mikolajewicz. “Stabilizing feed- backs allow for multiple states of the Greenland Ice Sheet in a fully coupled Earth System – Ice Sheet Model”. In:The Cryosphere20.2 (Feb. 2026), pp. 1047–1069.��: 1994-0424.��:��.��:�� . [And11] Theodore W...

2026

[2] [2]

Two decades of climate tipping points research: Progress and outlook

[Arm24] David I. Armstrong McKay. “Two decades of climate tipping points research: Progress and outlook”. In:Dialogues on Climate Change1.1 (Dec. 2024), pp. 30–38.��: 2976- 8659, 2976-8659.��:��. [BKV19] M. Brunetti, J. Kasparian, and C. V´ erard. “Co-existing climate attractors in a cou- pled aquaplanet”. In:Climate Dynamics53.9...

arXiv 2024

[3] [3]

Estimating fractal dimensions: A comparative review and open source implementations

[Dat+23] George Datseris et al. “Estimating fractal dimensions: A comparative review and open source implementations”. In:Chaos: An Interdisciplinary Journal of Nonlinear Science33.10 (Oct. 2023).��: 1089-7682.��:��.��:�� . [Dat18] George Datseris. “DynamicalSystems.jl: A Julia software library for chaos...

2023

[4] [4]

The Modular Arbitrary- Order Ocean-Atmosphere Model: MAOOAM v1.0

��:��. (Visited on 05/27/2025). [DDV16] Lesley De Cruz, Jonathan Demaeyer, and St´ ephane Vannitsem. “The Modular Arbitrary- Order Ocean-Atmosphere Model: MAOOAM v1.0”. In:Geoscientific Model Develop- ment9.8 (Aug. 2016), pp. 2793–2808.��: 1991-9603.��:�� . (Visited on 04/07/2025). [Dei+23] Russell Deitrick et...

2025

[5] [5]

ComplexityMeasures.jl: Scalable soft- ware to unify and accelerate entropy and complexity timeseries analysis

[DH25] George Datseris and Kristian Agasøster Haaga. “ComplexityMeasures.jl: Scalable soft- ware to unify and accelerate entropy and complexity timeseries analysis”. In:PLOS ONE20.6 JUNE (2025), pp. 1–21.��: 19326203.��:�� .��:��. [DLW23] George Datseris, Kalel Luiz Rossi, and Alexand...

2025

[6] [6]

Alternative signaling pathways: when, where and why?

[GZ05] Yunchen Gong and Zhaolei Zhang. “Alternative signaling pathways: when, where and why?” en. In:FEBS Lett.579.24 (Oct. 2005), pp. 5265–5274. [Ham+23] Ois´ ın Hamilton et al. “Multistability in a coupled ocean–atmosphere reduced-order model: Nonlinear temperature equations”. In:Quarterly Journal of the Royal Meteo- rological Society149.757 (Sept. 2023...

2005

[7] [7]

Demarcating circulation regimes of synchronously rotating terrestrial planets within the habitable zone

18 [Haq+18] Jacob Haqq-Misra et al. “Demarcating circulation regimes of synchronously rotating terrestrial planets within the habitable zone”. In:The Astrophysical Journal852.2 (2018), p

2018

[8] [8]

The Sparse Atmospheric Model Sampling Analysis (SAMOSA) Intercomparison: Motivations and Protocol Version 1.0: A CUISINES Model Intercom- parison Project

[Haq+22] Jacob Haqq-Misra et al. “The Sparse Atmospheric Model Sampling Analysis (SAMOSA) Intercomparison: Motivations and Protocol Version 1.0: A CUISINES Model Intercom- parison Project”. In:The Planetary Science Journal3.11 (Nov. 2022), p. 260.��: 2632-3338.��:��.��:�� . [Har+24] Carsten Hartmann et...

2022