Articles | Volume 21, issue 4
https://doi.org/10.5194/cp-21-773-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.Special issue:
Pattern scaling of simulated vegetation change in northern Africa during glacial cycles
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- Final revised paper (published on 10 Apr 2025)
- Preprint (discussion started on 18 Sep 2024)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on cp-2024-61', Anonymous Referee #1, 07 Nov 2024
- AC1: 'Reply on RC1', Mateo Duque-Villegas, 17 Jan 2025
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RC2: 'Comment on cp-2024-61', Shivangi Tiwari, 14 Nov 2024
- AC2: 'Reply on RC2', Mateo Duque-Villegas, 17 Jan 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (22 Jan 2025) by Manuel Chevalier

AR by Mateo Duque-Villegas on behalf of the Authors (04 Feb 2025)
Author's response
Author's tracked changes
Manuscript
ED: Publish as is (06 Feb 2025) by Manuel Chevalier

AR by Mateo Duque-Villegas on behalf of the Authors (12 Feb 2025)
Manuscript
The paper from Mateo Duque-Villegas et al. uses MPI-ESM v1.2 for the past 134 ka to simulate northern African hydroclimate and vegetation to reconstruct past AHPs. It aims to construct a pattern scale model to estimate AHPs of the past 800 ka. The model uses forcing variables (insolation, ice volume, and GHG) and is tested against proxy variables such as d18O (vs. 2K T), dP (vs. L*), and vegetation (vs. isotopic depletion and dust). Then, past AHPs are defined as peaks in the pan-Saharan vegetation coverage (%) and analyzed spatially. Here, EOFs capture dominant patterns of vegetation change, which are used to fit a linear model between the forcing variables and the PC1. They state orbitals influence around 66% of the linear model outcome. Next, the pattern scaling is applied and compared against MPI-ESM output, showing a trustworthy approach and extending the temporal extent back to 800 ka. The comparison with the Saporpel record from the Mediterranean does show a good agreement when using some arbitrary thresholding. Last, the pattern scaling approach and their MPI-ESP are used for future SSP scenarios with moderate and intensive GHG emissions for the coming ten kyr. Differences are observed, mainly when the pattern scaling linear model is used for the intense GHG emissions, as this quasi-empirical model has no data example for that.
The paper presents an insightful, well-written approach and uses well-curated figures to show its results. The paper structure is sometimes unusual with literature work and comparisons and discussions within the result section and, therefore, a short discussion section due to the linear progressing type of analysis. I like it, but others can see this differently. The length and the grade of detail are sufficient and allow fluent reading.
The method and the presented results seem logical and reasonable; no significant flaws were detected by my side, even though I am not an expert in GCMs, especially not for this model. Therefore, from my modest perspective, I see the paper as acceptable, but there are some suggestions and comments from my side:
Major comments:
The temporal framework in which the model results are presented is based on glacial cycles and the marine isotope stages, whereas the research target is mainly tropical. There are arguments to see this as problematic, as it can be a north-western perspective that acts as a framework to understand tropical climate, leading to false reasoning about forcing factors. They may also lead to false conclusions when they are used to discussing tropical climates if NH climate implications are applied. In contrast, cycles of the Monsoon Index could simply be counted and labeled, or absolute ages could be used.
The draft presents its results mainly as a cost-efficient estimation of past AHP patterns based on the simple linear fit. However, the fit itself is of interest, as it shows that orbitals are the main drivers of AHPs, not GHGs or ice sheets. Why is this not presented as an exciting result to be discussed?
Vegetation and vegetation feedback are non-linear and have multimodal distributions. However, EOF assumes unimodality and works better with a Gaussian distribution. How is this considered?
Minor Comments:
L5: Capitalize African Humid Period (AHP)?
L25: „1.7 ka to 4.2 ka,“ Where are these definite numbers from? There is a reference needed. Also, a broadened perspective on when, where, and how the AHP ends would be helpful (e.g., abrupt/ gradual, eastern vs. western Africa, or N-S transect, e.g., Shanahan et al., 2015)
L110: Marine sediment reflectance is rapidly introduced here and would need some more explanation for the non-marine audience
L115: More wording could be helpful to explain that the output of the model is compared to proxy results and other model insights
L145: Eastern Africa instead of East Africa, as East Africa refers to the colonial name, whereas eastern Africa is the geographical term
L165: If the last AHP, which has the best ground truth so far, is not correctly reconstructed, how do we assume the model works correctly?
L235: I think this is an exciting output of the study as it shows, with a simple linear model, the contributions of the forcing factors to Africa’s climate heartbeat. For the last sentence, I would pronounce the weakening of the orbital forcing without necessarily increasing NH forcing on tropical African climate.
L325: Indeed, pattern scaling is an empirical method, and there is no precursor to having the model trained on, so it is extrapolating; hence, reaction patterns to this GHG forcing are simply unknown.
L375: An AHP SW-NE tilt exists in the Krapp et al. (2021) dataset, foremost in the MAP. It is weak and underestimated compared to the terrestrial observations, but it exists.