Research ArticleCLIMATOLOGY

South Asian summer monsoon projections constrained by the interdecadal Pacific oscillation

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Science Advances  13 Mar 2020:
Vol. 6, no. 11, eaay6546
DOI: 10.1126/sciadv.aay6546
  • Fig. 1 SASM rainfall changes under the RCP8.5 scenario.

    June-July-August (JJA) mean rainfall trends under RCP8.5 scenario during 2016–2045 for (A) MPI-ESM 100-member ensemble mean, (B) intermember SD, (C) the mean trend of the 10 members with the driest trends, and (D) the mean trend of the 10 members with the wettest trends. Slant hatching denotes trends significant at the 95% confidence level. Units: mm day−1 (30 years)−1. The box in (A) to (D) highlights the SASM region (5°N to 30°N, 65°E to 90°E). (E) Time series of 9-year running mean of SASM rainfall anomalies (relative to the 1950–2005 mean). Historical (gray) and RCP8.5 (red) simulations are shown for the 5th and 95th percentiles (shading), the ensemble mean (thick solid lines), and the maximum and minimum (dashed lines) of the 100 members (units: mm day−1). The black line denotes the observational time series of the 9-year running mean of the SASM rainfall anomalies derived from the Global Precipitation Climatology Centre version v7 dataset. The 10 members with the wettest (blue) and driest (brown) trends during 2016–2045 are also shown together with its 5th and 95th percentile (shading). The inset figure in (E) shows the histogram of the SASM rainfall trends during 2016–2045 for the 100 MPI-ESM members under the RCP8.5 scenario [units: mm day−1 (30 years)−1]. The red, blue, and brown triangles denote the ensemble mean of the 100 members, the 10 wettest members, and the 10 driest members, respectively.

  • Fig. 2 Trends in SSTs and large-scale circulation associated with SASM rainfall trend spread under the RCP8.5 scenario.

    (A) Mean SASM rainfall trends differences between the 10 members with the driest and the wettest trends during 2016–2045 [units: mm day−1 (30 years)−1]. (B) SST trend differences [units: K (30 years)−1] and (D) trend differences of the sea level pressure [shading; units: hPa (30 years)−1], 200-hPa velocity potential [contours, units: m2 s−1 (30 years)−1], and 850-hPa winds [vectors; units: m s−1 (30 years)−1] during 2016–2045 between the 10 driest and the 10 wettest members of MPI-ESM simulation under the RCP8.5 scenario. Slant hatching denotes regions significant at the 95% confidence level. (C) Scatterplot between the standardized IPO index trends [x axis; units: (30 years)−1] and the SASM rainfall trends [y axis; units: mm day−1 (30 years)−1] among the MPI-ESM 100 members. Blue and brown dots denote the 10 wettest and 10 driest members, respectively.

  • Fig. 3 SASM rainfall trend histograms under the RCP8.5 scenario with and without IPO’s influences.

    (A) Histograms (bars) and 100-bins fitted distribution (lines) of the area-averaged rainfall trends over the SASM region (5°N to 30°N, 65°E to 90°E) derived from the 100 MPI-ESM ensemble members. The gray bars and the black fitted curves show the frequency of occurrence of the rainfall trends (with an SD of 0.40). The pink bars and the red fitted curves show the frequency of occurrence of the rainfall trends with the IPO’s influence being removed through linear regression against the IPO index in the individual runs (with a SD of 0.35). The black and red dots denote the ensemble mean of the distribution represented by the corresponding color. The black and red horizontal lines denote the 5th to 95th percentile range of 1.33 and 1.13 for the distribution represented by the corresponding color. (B) The gray bars and black curves are the same as (A), while the brown and blue bars and curves show the frequency of occurrence of the area-averaged rainfall trends with the same amplitude of a positive [+2 (30 years)−1] or a negative [−2 (30 years)−1] IPO phase transition, respectively, from 2016 to 2045. The black, brown, and blue dots denote the ensemble mean of the distribution with the corresponding color. The purple (brown) dashed line denotes the threshold of a chance of an extreme wetting (drying) trend.

  • Fig. 4 Uncertainty in the SASM rainfall changes under the RCP8.5 scenario in CanESM2.

    (A) Time series of 9-year running mean of the SASM rainfall anomalies relative to 1950–2005 mean similar to Fig. 1E but derived from the 50 CanESM2 members. (B and C) Histograms and 100-bins fitted distribution of the area-averaged SASM rainfall trends similar to Fig. 3 but for the 50 CanESM2 members.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/11/eaay6546/DC1

    Fig. S1. Evaluation of the historical SASM rainfall in MPI-ESM.

    Fig. S2. IPO indices and the associated SST anomalies.

    Fig. S3. The leading intermember EOF pattern of the SASM rainfall trends and the associated SST trend.

    Fig. S4. Trends of IPO indices.

    Fig. S5. Analogs among the 100 MPI-ESM ensemble members based on the IPO index trends.

    Fig. S6. The influence of IPO evolution on the SASM rainfall trends projection uncertainty under the RCP4.5 scenario.

    Fig. S7. The influence of IPO transition on the SASM rainfall trends projection uncertainty during 2016–2030 under the RCP8.5 scenario.

    Fig. S8. The trend uncertainty in the SASM rainfall and the associated SST trends under the RCP8.5 scenario in CanESM2.

    Fig. S9. AMO-related SASM rainfall and SST anomalies in the observations and MPI-ESM.

    Fig. S10. SASM rainfall trend during 2016–2045 under the RCP8.5 scenario without influences of the AMO.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Evaluation of the historical SASM rainfall in MPI-ESM.
    • Fig. S2. IPO indices and the associated SST anomalies.
    • Fig. S3. The leading intermember EOF pattern of the SASM rainfall trends and the associated SST trend.
    • Fig. S4. Trends of IPO indices.
    • Fig. S5. Analogs among the 100 MPI-ESM ensemble members based on the IPO index trends.
    • Fig. S6. The influence of IPO evolution on the SASM rainfall trends projection uncertainty under the RCP4.5 scenario.
    • Fig. S7. The influence of IPO transition on the SASM rainfall trends projection uncertainty during 2016–2030 under the RCP8.5 scenario.
    • Fig. S8. The trend uncertainty in the SASM rainfall and the associated SST trends under the RCP8.5 scenario in CanESM2.
    • Fig. S9. AMO-related SASM rainfall and SST anomalies in the observations and MPI-ESM.
    • Fig. S10. SASM rainfall trend during 2016–2045 under the RCP8.5 scenario without influences of the AMO.

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