Research ArticleCLIMATE CHANGE

Rainfall regimes of the Green Sahara

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Science Advances  18 Jan 2017:
Vol. 3, no. 1, e1601503
DOI: 10.1126/sciadv.1601503
  • Fig. 1 Location of the sediment cores used in this study and modern mean annual precipitation rates (in millimeters per year) (60).

    These cores were collected as part of the Changing Holocene Environments Eastern Tropical Atlantic (CHEETA) R/V Oceanus cruise (OCE437-7) in 2007. Site GC27: 30.88°N, 10.63°W, 1258-m water depth; GC37: 26.816°N, 15.118°W, 2771-m water depth; GC49: 23.206°N, 17.854°W, 2303-m water depth; GC68: 19.363°N, 17.282°W, 1396-m water depth.

  • Fig. 2 δDwax-­inferred δDP (A) and mean annual precipitation (in millimeters per year) (B) at each core site.

    Black lines indicate median values; colors indicate posterior probability distributions. Black squares with error bars denote modern mean annual observational values and SDs [from the Online Isotopes in Precipitation Calculator (61) for δDP and Global Precipitation Climatology Centre version 6 (60) for precipitation]. Note that, because our δDwax precipitation regression is logarithmic, the uncertainty of our inferred precipitation rates increases at higher precipitation amounts; thus, probability densities are more broadly distributed during wet intervals.

  • Fig. 3 Hovmöller diagrams of proxy-inferred and model-simulated precipitation in western Sahara.

    (A) δDwax-inferred mean annual precipitation. Asterisks denote the latitudinal locations of the core sites. (B) Mean annual precipitation from the TraCE experiment, conducted with the CCSM3 climate model (44). B/A, Bølling/Allerød period; YD, Younger Dryas; 8K, 8 ka “pause.”

  • Fig. 4 The 8 ka pause in Green Sahara conditions.

    Green areas denote the probability distributions of the start and end of the 8 ka dry period at sites GC49 (23°N) and GC68 (19°N), on the basis of the inferred locations in the core (from bioturbation modeling) for an abrupt beginning and end, and Monte Carlo iteration of age model uncertainties. Black and gray dots with error bars represent the mean and 1σ ages for climatic events associated with the 8.2 cooling event in the Northern Hemisphere, including (i) the timing of the drainage of Lake Agassiz and Lake Ojibway (37); (ii) the timing of an abrupt rise in sea level, detected in the Netherlands (62); (iii) the duration of the 8.2 event in the Greenland ice cores (38); and (iv) the duration of the response in Cariaco Basin grayscale data (63). Shown in red is the timing and duration of a prolonged interruption in the occupation of the Gobero site by humans (31). The yellow bar indicates a Sahara-wide demographic decline (32).

  • Fig. 5 Comparison between paleoclimate data and model simulations of mid-Holocene (6 ka) climate in the western Sahara.

    Model data represent 6 ka anomalies (relative to preindustrial control simulations) for land grid cells closest to the Atlantic coast along the given latitudes (y axis). Asterisks next to the model names (x axis) denote models with a dynamic vegetation module. PMIP2 and PMIP3 indicate models participating in the Paleoclimate Intercomparison Project Phase 2 and 3, respectively. The EC-Earth simulations are from the study by Pausata et al. (27). The data shown include both pollen-inferred precipitation data (16) and leaf wax–inferred precipitation data (this study). To overcome the paucity of data in the western Sahara, the pollen data represent average values across the entirety of North Africa for the given latitudes. X denotes no data available for the given latitude.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/1/e1601503/DC1

    Supplementary Materials and Methods

    table S1. Radiocarbon dates for the sediment cores used in this study.

    table S2. End-member δ13Cwax and ε values used for modeling δDP.

    table S3. List of paleoclimate data sets investigated for the presence of an 8 ka dry event.

    table S4. List of the climate models used for model-data comparison.

    fig. S1. Estimated values for δDP versus δDwax- ­ and δDwax ­-inferred δDP.

    fig. S2. Regional relationship between δDP and precipitation amount.

    fig. S3. Changes in sea-level pressure and precipitation in the GS-RD experiment during boreal winter.

    fig. S4. Bioturbation forward modeling experiments.

    fig. S5. Probability distributions of the end of the Green Sahara at each core site.

    fig. S6. The presence and duration of the 8 ka event across North and East Africa.

    fig. S7. Age models for each of the core sites.

    fig. S8. δDwax and δ13Cwax for each of the core sites.

    fig. S9. Map of the core top sediments used for δDP validation and the precipitation regression model.

    fig. S10. Prior and posterior probability distributions for the parameters of the Bayesian regression model.

    References (64104)

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Materials and Methods
    • table S1. Radiocarbon dates for the sediment cores used in this study.
    • table S2. End-member δ13Cwax and ε values used for modeling δDP.
    • table S3. List of paleoclimate data sets investigated for the presence of an 8 ka dry event.
    • table S4. List of the climate models used for model-data comparison.
    • fig. S1. Estimated values for δDP versus δDwax- andδDwax-inferred δDP.
    • fig. S2. Regional relationship between δDP and precipitation amount.
    • fig. S3. Changes in sea-level pressure and precipitation in the GS-RD experiment during boreal winter.
    • fig. S4. Bioturbation forward modeling experiments.
    • fig. S5. Probability distributions of the end of the Green Sahara at each core site.
    • fig. S6. The presence and duration of the 8 ka event across North and East Africa.
    • fig. S7. Age models for each of the core sites.
    • fig. S8. δDwax and δ13Cwax for each of the core sites.
    • fig. S9. Map of the core top sediments used for δDP validation and the precipitation regression model.
    • fig. S10. Prior and posterior probability distributions for the parameters of the Bayesian regression model.
    • References (64–104)

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