Holocene evolution of Saharan vegetation revealed by paleoclimate simulations and machine learning

The Greening of the Sahara (GS) during the African Humid Period (AHP) is a striking example of past climate–vegetation interactions, yet its detailed spatiotemporal patterns across the Holocene remain insufficiently understood. This study develops a data-driven framework that combines paleoclimate simulations with machine learning to reconstruct vegetation dynamics in North Africa. Two machine learning models, an artificial neural network (ANN) and a random forest (RF), were trained on observed nonlinear relationships between vegetation and climate variables. These models were applied to paleoclimate proxy data and the transient climate simulation TraCE-21ka to estimate Holocene normalized difference vegetation index (NDVI). The ANN model outperformed RF in representing complex vegetation–climate linkages and showed closer agreement with proxy evidence. ANN-based reconstructions indicate a rapid expansion of vegetation in North Africa and the Arabian Peninsula following the Younger Dryas (∼12,000 years BP), sustained high vegetation cover during the AHP (10,000–6,200 years BP), and a gradual decline thereafter. However, the ANN underestimated both the overall vegetation cover and the abrupt decline around 6,000 years BP suggested by proxy data. Sensitivity analyses highlight monsoon-driven precipitation as the primary control on vegetation change, with temperature exerting a secondary but reinforcing influence. This machine learning–based framework provides a new perspective for investigating vegetation responses to past and future climate change. • Developed a data-driven framework integrating paleoclimate simulations and machine learning, enabling high-resolution reconstruction of Holocene Saharan vegetation dynamics in a computationally efficient manner. • Revealed a three-phase evolution of Saharan vegetation: rapid expansion (∼12–10 ka BP), sustained high cover (10–6.2 ka BP), and gradual decline—driven primarily by precipitation, with temperature as a secondary factor and minimal influence from CO 2 . • Provided evidence that the Green Sahara played a supporting role in agricultural development and cultural expansion across Eurasia.

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Publication Details

Journal
Information Geography
Published
2026-02-10
DOI
https://doi.org/10.1016/j.infgeo.2026.100042
Primary Topic
Geology and Paleoclimatology Research
Type
article
Field-Weighted Citation Impact
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article

Holocene evolution of Saharan vegetation revealed by paleoclimate simulations and machine learning

Cheng Shen, Jian Liu, Xi Zhang, Weiyi Sun et al.
Information Geography
Geology and Paleoclimatology Research
article

Holocene evolution of Saharan vegetation revealed by paleoclimate simulations and machine learning

Cheng Shen, Jian Liu, Xi Zhang, Weiyi Sun, Bo Lu, Guangyao Hao, Deliang Chen, Guonian Lü, Liang Ning
article en

Abstract

The Greening of the Sahara (GS) during the African Humid Period (AHP) is a striking example of past climate–vegetation interactions, yet its detailed spatiotemporal patterns across the Holocene remain insufficiently understood. This study develops a data-driven framework that combines paleoclimate simulations with machine learning to reconstruct vegetation dynamics in North Africa. Two machine learning models, an artificial neural network (ANN) and a random forest (RF), were trained on observed nonlinear relationships between vegetation and climate variables. These models were applied to paleoclimate proxy data and the transient climate simulation TraCE-21ka to estimate Holocene normalized difference vegetation index (NDVI). The ANN model outperformed RF in representing complex vegetation–climate linkages and showed closer agreement with proxy evidence. ANN-based reconstructions indicate a rapid expansion of vegetation in North Africa and the Arabian Peninsula following the Younger Dryas (∼12,000 years BP), sustained high vegetation cover during the AHP (10,000–6,200 years BP), and a gradual decline thereafter. However, the ANN underestimated both the overall vegetation cover and the abrupt decline around 6,000 years BP suggested by proxy data. Sensitivity analyses highlight monsoon-driven precipitation as the primary control on vegetation change, with temperature exerting a secondary but reinforcing influence. This machine learning–based framework provides a new perspective for investigating vegetation responses to past and future climate change. • Developed a data-driven framework integrating paleoclimate simulations and machine learning, enabling high-resolution reconstruction of Holocene Saharan vegetation dynamics in a computationally efficient manner. • Revealed a three-phase evolution of Saharan vegetation: rapid expansion (∼12–10 ka BP), sustained high cover (10–6.2 ka BP), and gradual decline—driven primarily by precipitation, with temperature as a secondary factor and minimal influence from CO 2 . • Provided evidence that the Green Sahara played a supporting role in agricultural development and cultural expansion across Eurasia.

Information GeographyVol. 2(2)
China Meteorological Administration (CN), Nanjing Normal University (CN), Nanjing University of Information Science and Technology (CN), Shaanxi Provincial Meteorological Bureau (CN), Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application (CN), Ministry of Education (BD), University of Gothenburg (SE), Tsinghua University (CN)
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Geology and Paleoclimatology Research
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