Empirical reconstruction of African Humid Periods across the Quaternary

Wang, Hang; Yang, Hu; Shi, Xiaoxu; Liu, Jiping; Lohmann, Gerrit; Wu, Jiawang; Tian, Jun; Cruz, Francisco W.; Cheng, Jianbo; Chen, Dake

doi:10.5194/egusphere-2026-5495

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https://doi.org/10.5194/egusphere-2026-5495

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29 Sep 2026

| 29 Sep 2026

Status: this preprint is open for discussion and under review for Climate of the Past (CP).

Empirical reconstruction of African Humid Periods across the Quaternary

Hang Wang, Hu Yang, Xiaoxu Shi, Jiping Liu, Gerrit Lohmann, Jiawang Wu, Jun Tian, Francisco W. Cruz, Jianbo Cheng, and Dake Chen

Abstract. As Earth’s largest desert, the Sahara experienced periodic humid periods in the geological past, known as African Humid Periods (AHP), which have nurtured the earliest human civilizations. Reconstructing the climatic evolution of the Sahara is therefore important not only for understanding the region's own climate history but also for addressing archaeological questions related to human evolution and migration. Traditionally, the paleoclimate of this region has been reconstructed primarily from geological records, but such reconstructions are costly to obtain and are often subject to considerable chronological uncertainty. Numerical simulations can partly compensate for these limitations but also demand substantial computational resources when reconstructing climate variability over long timescales. To address these challenges, we conducted a series of climate sensitivity experiments to quantify the contributions of different climate forcings, including Earth's orbital, atmospheric CO₂, and ice sheets, to AHP formation. Based on these experiments, we developed a physically based, empirical model that reconstructs the pacing of AHP occurrence using a few key metrics: orbital parameters, CO₂ concentration, and global sea level. We validated this framework against geologic AHP reconstructions spanning the past 800 kilo-years (ka), and further applied it to reconstruct AHP throughout the entire Quaternary. This computationally efficient framework offers a new tool for reconstructing the long-term paleoclimatic evolution of the Sahara, with the potential to refine the chronology of North African geological and archaeological records.

Received: 11 Sep 2026 – Discussion started: 29 Sep 2026

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Hang Wang, Hu Yang, Xiaoxu Shi, Jiping Liu, Gerrit Lohmann, Jiawang Wu, Jun Tian, Francisco W. Cruz, Jianbo Cheng, and Dake Chen

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RC1:
'Comment on egusphere-2026-5495', Anonymous Referee #1, 02 Nov 2026 reply
General comment
In this manuscript Wang et al. present a novel physics-based framework to predict the timing of past African humid periods (AHPs) using known forcing factors like orbital geometry, CO₂ levels, ice-sheet extent and mean sea level. To construct this framework the authors took advantage of an ensemble of sensitivity experiments to disentangle the effects of individual forcings, and thus be able to parameterize each one into models for the Inter-Tropical Convergence Zone (ITCZ) position, Saharan summer precipitation and vegetation cover. Wang et al. first assess prediction skill against proxies for AHPs, as well as against previous climate modelling estimates, and then apply their method to the entire Quaternary.
First, I would like to clarify that I also previously reviewed an earlier version of this manuscript submitted elsewhere. I am glad to find that constructive feedback has been incorporated into this new submission, and I think the new outline and focus make up a clearer message. Second, I think this is a nice theoretical contribution to the study of Sahara greening and is well within the scope of Climate of the Past. Below I list minor comments and editorial remarks that I think may be useful considerations.
Minor comments
Key to this work are many sensitivity simulations with AWI-ESM. Have the authors checked the spin-up time was enough for the FESOM ocean to reach quasi-equilibrium after 1000 years? Because the sensitivity experiments change forcing conditions so drastically, I think it would take the ocean longer to adjust to the new energy and mass fluxes. It does not go against what the authors find, but it is another caveat to consider.

I would summarize the empirical model as: Saharan greening is a function of summer rainfall, which itself is a function of the position of the ITCZ, which itself is a function of forcing factors. A core assumption is then that the ITCZ position defines AHP occurrence. Previously I had made the suggestion to review this assumption by considering other circulation patterns that are critical for rainfall over the Sahara such as the African Easterly and the Tropical Easterly jets (Nicholson, 2009). Because of mass continuity, I expect that what the authors find still holds, even if the ITCZ position over land is not itself defining precipitation amounts, but I would like to raise the point again that these other complexities be taken into account.

And related to the ITCZ, in Section 2.3.1 it is not clear to me how the ITCZ mean position was actually defined. Was it based on mass convergence at pressure levels or was it based on maximum tropical precipitation? Because precipitation is key to the AHP definition, I would find it more sensible to define the ITCZ latitude based on a precipitation centroid, such as in, for instance, Donohoe et al. (2013) or in Adam et al. (2016).

Please provide in the supplementary material or in an appendix a diagram of the orbital geometry as explained by the authors, with the emphasis on the Earth-Sun distance at the summer solstice. The diagrams on Figure 1 are useful, but they do not help to understand the frame of reference that is key to defining angles like θ and α.

Please justify the selection of the nonlinear relationships that make up the empirical model. How were these relationships in equations 16 and 17 found? Only briefly mention the underlying model being used.

I think that there might be an error in the definition of the empirical modifier of ice sheet and sea-level. The authors use the term "Northern Hemispheric Ice Sheet" as producing an equivalent sea-level rise of 7 m. But this only the estimate for Greenland alone. So, either the empirical model is based only on Greenland, or please adjust this to consider the more or less 120 m of sea-level variation along glacial cycles, when Laurentide and Fennoscandian ice sheets (or alike) also came into play.

Another important caveat is to know that the sensitivity experiments do not fully disentangle the forcing factors, since conditions like C180 or C284, or other preindustrial fixed values may lie above or below thresholds for nonlinear behaviour. This does not invalidate the first-order approach of the authors.

Editorial remarks
- L21 :: maybe simpler "habitability" instead of "human civilization".
- L60 :: I think this is left over from an earlier version of the manuscript. So far in the text the concept of "Green Sahara" has not been defined. I would make explicit the relationship between AHP and Green Sahara first. This also connects to the label in Figure 1. And then consider adding a reference that names it "Green Sahara".
- L66 :: please add a version number to JSBACH. Either 3.2 or 4. I do not know which one AWI-ESM is using, but there are two different versions in use. Please note that references Brovkin et al. (2013) and Schneck et al. (2022) relate to the different versions.
- L75 :: I can only count 28 experiments. Which are the 2 that I am missing?
- Section 2.1 :: experiments Omax and Omin are not explained. Please briefly mention them after explaining Imax.
- L77 :: mention here the connection between precession phase and the longitude of the perihelion, which is the angle that is being changed.
- L79 :: I would say "... ice sheets" in plural, and instead of "the condition in" I would suggest "a reconstruction of".
- Table 1 :: I think these are experiment _categories_. And please include in the caption the reference preindustrial values for comparison. And also in the table I would use the plural for ice sheets.
- Figure 1 :: please include the panel labels in the caption. And consider defining Green Sahara in the main text or replacing it with AHP.
- Figure 3 :: please explain "DNS" in the caption. I assume it is dynamical normalized seasonality.
- L191 :: math mode for focal length "c", so $c$.
- L196 :: I could not find this equation in the Wallace and Hobbs (2006) text. However it is very close to equation 2.17 in Hartmann (2016).
- L204 :: why capitalization in "Surface"? And also maybe consider changing the symbol theta for terrestrial longitude here, since it is already being used for the polar angle in equation 2. Also notice that R2 is undefined.
- Equation 6 :: I would suggest to use \overbrace or \underbrace to annotate these equation parts. Otherwise it looks like a division.
- L220 :: I would suggest to add at the end after Q_NH-SH: "at the boreal summer solstice".
- L273 :: vegetation _cover_
- L275 :: 24 or 23? In Figure 7 I only see 23.
- Figure 7 :: explain why number 12 is counted as an AHP. Maybe it touches the threshold line, does it?
- L299 :: Armstrong et al. (2023) is not a "long transient simulation", but rather a sequence of time-slice simulations spliced together.
- L308 :: gives
- Figure 9 :: consider making the threshold line slightly thicker, because when on paper it is difficult to make out.
References
Adam, O., Bischoff, T., & Schneider, T. (2016). Seasonal and interannual variations of the energy flux equator and ITCZ. Part I: Zonally averaged ITCZ position. Journal of Climate, 29(9), 3219–3230. doi:10.1175/jcli-d-15-0512.1
Armstrong, E., Tallavaara, M., Hopcroft, P. O., & Valdes, P. J. (2023). North African humid periods over the past 800,000 years. Nature Communications, 14(1). doi:10.1038/s41467-023-41219-4
Brovkin, V., Boysen, L., Raddatz, T., Gayler, V., Loew, A., & Claussen, M. (2013). Evaluation of vegetation cover and land‐surface albedo in MPI‐ESM CMIP5 simulations. Journal of Advances in Modeling Earth Systems, 5(1), 48–57. doi:10.1029/2012ms000169
Donohoe, A., Marshall, J., Ferreira, D., & Mcgee, D. (2013). The relationship between ITCZ location and cross-equatorial atmospheric heat transport: from the seasonal cycle to the Last Glacial Maximum. Journal of Climate, 26(11), 3597–3618. doi:10.1175/jcli-d-12-00467.1
Hartmann, D. L. (2016). Global physical climatology (Second, p. 485). Amsterdam, Netherlands: Elsevier.
Nicholson, S. E. (2009). A revised picture of the structure of the “monsoon” and land ITCZ over West Africa. Climate Dynamics, 32(7-8), 1155–1171. doi:10.1007/s00382-008-0514-3
Schneck, R., Gayler, V., Nabel, J. E. M. S., Raddatz, T., Reick, C. H., & Schnur, R. (2022). Assessment of JSBACHv4.30 as a land component of ICON-ESM-V1 in comparison to its predecessor JSBACHv3.2 of MPI-ESM1.2. Geoscientific Model Development, 15(22), 8581–8611. doi:10.5194/gmd-15-8581-2022
Wallace, J. M., & Hobbs, P. V. (2006). Atmospheric science: An introductory survey. On International Geophysics Series (Second, p. 483). Amsterdam, Netherlands: Academic Press.

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Citation: https://doi.org/10.5194/egusphere-2026-5495-RC1

Hang Wang, Hu Yang, Xiaoxu Shi, Jiping Liu, Gerrit Lohmann, Jiawang Wu, Jun Tian, Francisco W. Cruz, Jianbo Cheng, and Dake Chen

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Hang Wang, Hu Yang, Xiaoxu Shi, Jiping Liu, Gerrit Lohmann, Jiawang Wu, Jun Tian, Francisco W. Cruz, Jianbo Cheng, and Dake Chen

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Short summary

Geological records of past Saharan climate are often incomplete and difficult to date, while long climate simulations require extensive computing. We therefore developed an efficient empirical model from 30 climate simulations and records of Earth's orbit, carbon dioxide and sea level. It provides a practical way to study long-term Saharan climate change and can guide future simulations, field sampling and the dating of geological and archaeological records.


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