Research ArticleCLIMATOLOGY

Emergence of an equatorial mode of climate variability in the Indian Ocean

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Science Advances  06 May 2020:
Vol. 6, no. 19, eaay7684
DOI: 10.1126/sciadv.aay7684
  • Fig. 1 Observed variability and mean state over the tropical oceans.

    (A) SST variability, (B) annual mean subsurface ocean temperature along the equator (5°S to 5°N), and (C) annual mean SST (shading) and surface wind stress (vectors). SST variability is computed as the SD of monthly anomalies relative to the monthly mean seasonal cycle. In the tropical oceans, a metric of variability that is dominated by variations occurring on interannual time scales. SST and surface wind stress are from TropFlux (46) and subsurface ocean temperature data are from ORAS-S4 (37).

  • Fig. 2 Simulated changes in IO climate variability and mean state under glacial conditions and greenhouse warming.

    Changes in (A and B) SST variability, (C and D) subsurface ocean temperature (shading, m), vertical velocity (contours, m/day), and (E and F) SST (shading) and surface wind stress (vectors). Glacial changes (left) are computed from a simulation of LGM relative to a simulation of preindustrial (PI) climate, both performed with the CESM1. Changes under greenhouse warming are computed for the 2050–2100 interval in high-emission scenario [Representative Concentration Pathway 8.5 (RCP8.5)] simulations performed by 36 CMIP5 models relative to the 1850–1950 interval from historical simulations. The changes in variability are computed as the difference in SD of SSTAs during the August-September-October (ASO) season. Changes in mean state are computed for the JAS season. The changes under greenhouse warming are the average among the changes simulated among all 36 CMIP5 models. Dashed and solid red curves in (C) and (D) indicate the depth of thermocline in the reference (PI and historical) and altered (LGM and RCP8.5) climate states, respectively. (G) Relationship between changes in SD of SST anomalies in the EEIO (70°E to 95°E, 2.5°S to 2.5°N) during the ASO season and zonal wind stress in the equatorial IO (50°E to 80°E, 2.5°S to 2.5°N) during the JAS season for each model simulated response to greenhouse warming (blue circles) and LGM boundary conditions (red circle). Models with mode activation are outlined in red.

  • Fig. 3 Atmospheric precursor and developed warm equatorial mode events under greenhouse warming, glacial, and historical conditions.

    First column: SST (shading), surface wind stress (vectors), and thermocline depth (contours) anomalies associated with the atmospheric precursor of warm equatorial mode (A) in a subset of CMIP5 greenhouse warming simulations, (C) a LGM simulation, and (E) historical observations. Second column: Same as first column but for the peak of the event, 3 months after the occurrence of the precursor. Warm equatorial events are triggered by the atmospheric precursor under (B) greenhouse warming and (D) LGM conditions, but not (F) under historical conditions because the mean state is not conducive for coupled interactions. Events are identified and composited, when the April-May-June standardized zonal wind stress anomalies averaged over the western IO (40°E to 60°E, 2.5°S to 2.5°N) exceeds 0.5. The precursor phase is during May and the peak in August. The LGM simulation has modes of variability disabled as described in the “Methods” section.

  • Fig. 4 Rainfall impacts of current and future modes of climate variability in the IO.

    Composite rainfall anomalies (shading) during (A, B) observed Dipole Mode events and (C, D) simulated Equatorial Mode events active in the IO under greenhouse warming. In both cases, warm (A, C) and cold (B, D) events are, respectively, characterized by positive or negative SST anomalies (contours) over the eastern IO. SST contour interval is 0.25 K. Equatorial Mode events show rainfall and SST anomalies spanning much of the equatorial IO. Anomalies correspond to the peak season of each mode, September-October-November (SON) for the Dipole Mode and August-September-October for the Equatorial Mode. Observed Dipole Mode events are selected and composited on the basis of SON values of the Dipole Mode Index (18) with a 0.5σ threshold. Equatorial Mode events are selected and composited on the basis of indices of the western IO atmospheric precursor and the peak SSTA in the EEIO during the ASO season with a 0.5σ threshold (see “Data” and “Methods” sections). Both criteria combined isolate events that evolve into large-scale SST anomalies. Dipole Mode composites are based on the Global Precipitation Climatology Project (42) and TropFlux (36) observational datasets over the 1980–2017 period. Equatorial Mode composites are based on output from CMIP5 rcp85 simulations over the 2050–2100 period composited for each model run and then averaged across the 10 models with mode activation.

Supplementary Materials

  • Supplementary Materials

    Emergence of an equatorial mode of climate variability in the Indian Ocean

    Pedro N. DiNezio, Martin Puy, Kaustubh Thirumalai, Fei-Fei Jin, Jessica E. Tierney

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