Research ArticleOCEANOGRAPHY

North Atlantic salinity as a predictor of Sahel rainfall

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Science Advances  06 May 2016:
Vol. 2, no. 5, e1501588
DOI: 10.1126/sciadv.1501588
  • Fig. 1 Springtime Atlantic SSSA and Sahel monsoon-season rainfall.

    (A and B) The leading SVD mode of springtime (March to May) Atlantic SSSA (A) and June-to-September African precipitation (B). Shading indicates where the loading of the SVD mode is significant at the α = 0.05 level. The North Atlantic and South Atlantic regions are marked on (A), and the box in (B) denotes the Sahel region. (C) Time series of the first SVD modes of SSSA (solid red curve) and precipitation (solid blue curve), as well as the normalized March-to-May SSSA in the North Atlantic region (dashed red curve) and June-to-September Sahel precipitation (dashed blue curve).

  • Fig. 2 North Atlantic SSSA leads Sahel precipitation.

    (A and B) Correlation between June-to-September African precipitation and (A) wintertime [January-February-March (JFM)] and (B) springtime (MAM) SSSA over the North Atlantic. Areas with correlation coefficients significant at the α = 0.05 level are hatched. The effective degrees of freedom used to determine the significance level are calculated using tools described in Materials and Methods.

  • Fig. 3 Mechanisms linking springtime SSSA and Sahel monsoon-season precipitation.

    (A and B) Moisture flux divergence anomaly (shaded; mm day−1) and the divergent component of moisture flux (vectors; kg m−1 s−1) composites on North Atlantic SSSA: (A) March-to-May composite; (B) June-to-September composite. Vectors are only shown for moisture flux anomalies significant at the 0.05 level according to a Hotelling t2 test (65). The purple boxes denote the subtropical North Atlantic. (C and D) Moisture flux convergence anomaly (C) and soil moisture (SM) content anomaly (D) over the Sahel composited on springtime North Atlantic SSSA. The green bars are composites of high-SSS events, and the brown bars are those of low-SSS events. The high-SSS events and low-SSS events are selected as the top and bottom decile of the North Atlantic MAM SSS time series. The error bars denote the upper and lower bound of soil moisture or moisture flux convergence (MFC) anomaly defined by 1 SD. AMJ, April-May-June; MJJ, May-June-July; JJA, June-July-August; JAS, July-August-September; ASO, August-September-October; SON, September-October-November; OND, October-November-December.

  • Fig. 4 Sahel rainfall prediction using North Atlantic SSSA.

    (A) Box-and-whisker plot of the importance of predictors for Sahel monsoon-season precipitation according to 1000 trials of the random forest regression. Definition of the importance factor is in the Materials and Methods. Med. SSTA, Mediterranean SSTA; AMO, Atlantic Multidecadal Oscillation; Nino34, Niño 3.4; Atl. Nino, Atlantic Niño; SAOD, South Atlantic Ocean Dipole; IOD, Indian Ocean Dipole. (B and C) Precipitation anomaly (bold black curves; mm day−1) over the Sahel predicted by the random forest algorithm: (B) prediction using the combination of all predictors including SSSA (Materials and Methods); (C) prediction without the SSSA predictor. The light blue envelope is the 95% confidence interval derived from 1000 trials of the regression. The red curve is the observed precipitation anomaly. The Sahel precipitation predicted by each predictor is shown in fig. S8.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/5/e1501588/DC1

    Supplementary Materials and Methods

    fig. S1. SSSA-Sahel rainfall relationship independent of precipitation data sets.

    fig. S2. Evaluation of SSSA-Sahel rainfall relationship using salinity.

    fig. S3. Relationship between South Atlantic SSSA and Sahel precipitation.

    fig. S4. Land surface moisture balance in the Sahel.

    fig. S5. Consistency of soil moisture feedback mechanism as shown by NOAA CPC and ESA CCI products.

    fig. S6. North Atlantic SSSA–Sahel precipitation mechanism verified by remote sensing (SMOS) soil moisture data.

    fig. S7. SSSA-rainfall relationship independent of SSTA.

    fig. S8. Random forest regression using eight predictors.

    fig. S9. Linear model selection to predict Sahel monsoon-season precipitation.

    fig. S10. Linear model prediction of Sahel monsoon-season precipitation.

    table S1. Four precipitation data sets used in this study.

    table S2. SSTA-based predictors used to construct the random forest regression model for Sahel monsoon-season precipitation.

    table S3. Pairwise cross correlation between SSS mode, precipitation mode, North Atlantic MAM salinity, and Sahel JJAS precipitation.

    References (6680)

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Materials and Methods
    • fig. S1. SSSA-Sahel rainfall relationship independent of precipitation data sets.
    • fig. S2. Evaluation of SSSA-Sahel rainfall relationship using salinity.
    • fig. S3. Relationship between South Atlantic SSSA and Sahel precipitation.
    • fig. S4. Land surface moisture balance in the Sahel.
    • fig. S5. Consistency of soil moisture feedback mechanism as shown by NOAA CPC and ESA CCI products.
    • fig. S6. North Atlantic SSSA–Sahel precipitation mechanism verified by remote sensing (SMOS) soil moisture data.
    • fig. S7. SSSA-rainfall relationship independent of SSTA.
    • fig. S8. Random forest regression using eight predictors.
    • fig. S9. Linear model selection to predict Sahel monsoon-season precipitation.
    • fig. S10. Linear model prediction of Sahel monsoon-season precipitation.
    • table S1. Four precipitation data sets used in this study.
    • table S2. SSTA-based predictors used to construct the random forest regression model for Sahel monsoon-season precipitation.
    • table S3. Pairwise cross correlation between SSS mode, precipitation mode, North Atlantic MAM salinity, and Sahel JJAS precipitation.
    • References (66–80)

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