Research ArticleCLIMATOLOGY

Seasonal prediction of Indian wintertime aerosol pollution using the ocean memory effect

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Science Advances  17 Jul 2019:
Vol. 5, no. 7, eaav4157
DOI: 10.1126/sciadv.aav4157
  • Fig. 1 Spatial features of mean wintertime AOD and trend.

    (A) The MODIS (Moderate Resolution Imaging Spectroradiometer) Terra/Aqua observed AOD at 550 nm in DJF (December, January, and February) averaged over 2003–2018 (December 2002, January 2003, and February 2003 are considered as winter of 2003). (B) DJF AOD trend (unitless/year) over 2003–2018 (black dots denote areas with significant trend; P < 0.05).

  • Fig. 2 Features of the first two leading modes.

    Spatial patterns of EOF1 (A), EOF2 (B), and PCs (C) of the two leading EOF modes.

  • Fig. 3 Correlations between the first two modes and SST.

    (A) PC1-DJF SST correlation. (B) PC2-DJF SST correlation (black dots denote areas with significant correlation; P < 0.05).

  • Fig. 4 Connections between wind fields and climate variability.

    (A) Winter winds averaged over 2003–2018 at 850 mbar. (B) Vector correlation map of winter winds at 850 mbar with reference to autumn Niño 3 index. (C) Vector correlation map of winter winds at 850 mbar with reference to Indian Ocean Meridional Dipole index (IOMDI). (D) Correlation map of winter wind speeds at 10 m with reference to autumn Niño 3 index. (E) Correlation map of winter wind speeds at 10 m with reference to IOMDI.

  • Fig. 5 CESM simulated responses of concentrations of near-surface aerosol and AOD.

    (A) CESM simulated response of concentrations of total particulate matter (kg/kg) and winds (reference, 1 m/s) at 850 mbar to El Niño–like SST. (B) CESM simulated response of concentrations of total particulate matter (kg/kg) and winds (reference, 1 m/s) at 850 mbar to IOMD-like SST. (C) CESM simulated response of AOD to El Niño–like SST. (D) CESM simulated response of AOD to IOMD-like SST.

  • Fig. 6 Multivariable regression modeling.

    Time series of AOD anomaly for Northern India are represented in red. Results obtained using the regression model are indicated in blue. The hindcast AOD anomaly by the k-fold cross-validation method is denoted in green.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/7/eaav4157/DC1

    Fig. S1. Fractional variance (%) explained by the first six EOF modes of winter AOD in Northern India.

    Fig. S2. Interannual variability of DJF AO index and PC3.

    Fig. S3. Correlation between boundary layer heights and Niño 3/IOMD indices.

    Fig. S4. Correlation coefficients between the autumn AAO index and zonal mean wind speeds at 10 m in the Indian Ocean.

    Fig. S5. Correlation between autumn AAO and autumn/winter SST.

    Fig. S6. CESM simulated response of precipitation rate (m/s) to IOMD-like SST.

    Fig. S7. Mean responses of AOD to El Niño and positive AAO using the outputs of CESM LENS simulations (35 ensemble members).

    Table S1. AIC values for different combinations of predictors.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Fractional variance (%) explained by the first six EOF modes of winter AOD in Northern India.
    • Fig. S2. Interannual variability of DJF AO index and PC3.
    • Fig. S3. Correlation between boundary layer heights and Niño 3/IOMD indices.
    • Fig. S4. Correlation coefficients between the autumn AAO index and zonal mean wind speeds at 10 m in the Indian Ocean.
    • Fig. S5. Correlation between autumn AAO and autumn/winter SST.
    • Fig. S6. CESM simulated response of precipitation rate (m/s) to IOMD-like SST.
    • Fig. S7. Mean responses of AOD to El Niño and positive AAO using the outputs of CESM LENS simulations (35 ensemble members).
    • Table S1. AIC values for different combinations of predictors.

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