Research ArticleATMOSPHERIC SCIENCE

North American April tornado occurrences linked to global sea surface temperature anomalies

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Science Advances  21 Aug 2019:
Vol. 5, no. 8, eaaw9950
DOI: 10.1126/sciadv.aaw9950
  • Fig. 1 Influence of SSTA on the atmosphere and its relationship with U.S. tornado activity in April and May.

    (A to D) The SVD analysis is conducted on the basis of AMIP-simulated and NCEP1 reanalysis (OBS) year-to-year variabilities of the 500-hPa geopotential height anomalies (Z500′) over the northern hemisphere (20°N to 70°N and 0°E to 360°E) for April (A and C) and May (B and D), respectively (see Materials and Methods). (E and F) Regressed patterns of Z500′ and SSTAs on tornado numbers aggregated over the SGP region (30°N to 40°N and 100°W to 90°W; red box) for April (left) and May (right). Solid (dashed) lines indicate positive (negative) values. The intervals are 0.08 K for SSTA and 4 m for Z500′. The stippled (cross-hatched) areas indicate values for which the local null hypothesis of zero regression can be rejected at the 95% level for Z500′ (SSTA), based on a Student’s t test with 27 and 61 degrees of freedom for AMIP and observations, respectively. Long-term linear trends were removed before statistical analysis.

  • Fig. 2 Year-to-year changes in the number of tornadoes, low-level jet, and moisture flux convergence over the SGP.

    Observed tornado number anomalies over the SGP region (red), meridional wind anomalies (blue) over the south of the SGP (V925_S, in meters per second; 20°N to 30°N and 100°W to 90°W), and moisture flux convergence anomalies at 925 hPa (MFC925, 10−3 g/kg per second) over the SGP (purple) from 1954 to 2016 for (A) April and (B) May. The V925_S and MFC925 were calculated on the basis of two datasets—NCEP1 (solid line) and European Centre for Medium-Range Weather Forecasts 20th century reanalysis (ERA-20C; dashed line). Correlation coefficients (R) between tornado number anomalies and climate indices are shown in the top left corner (R values with ERA-20C are in braces). Long-term linear trends were removed.

  • Fig. 3 Large-scale patterns controlling the low-level jet from the Gulf of Mexico and moisture over the SGP.

    Linear regression patterns of Z500′ (contour) and SSTAs (shaded areas) onto (A and B) V925_S and (C and D) MFC925 for April (left) and May (right). The contour intervals are 0.08 K for SSTA and 8 m for Z500′. The stippled (cross-hatched) area indicates values for which the local null hypothesis of zero regression can be rejected at the 95% level for Z500′ (SSTA), based on a Student’s t test with 61 degrees of freedom. Long-term linear trends were removed before any statistical analysis.

  • Fig. 4 Thermodynamic variables during strong and weak April tornado years.

    Monthly climatology of the (A) MFC925 and (B) cPrcp over the SGP from all years (black line), strong April tornado years (red line), and weak April tornado years (blue line). The strong (weak) April tornado years are selected on the basis of 1.5 (−1.1) SDs. The selected strong years are 1957, 1964, 1979, 1982, 1991, and 2011, and the weak years are 1959, 1987, 1992, 1997, and 2007. MFC925 is obtained from NCEP1 for 1954–2016, and cPrcp is obtained from the NARR dataset for 1979–2016. Gray-shaded area encompasses 1 SD of all years. Because of the different time frames that each dataset covers, missing years are omitted from the composite mean calculation. Two representative strong and weak tornado years are indicated by the red and blue stars, respectively.

  • Fig. 5 Schematics illustrating enhancement in April tornado activity due to SST.

    Red arrows represent the sequence of the deterministic (and potentially predictable) climate influences on April tornado occurrence in the SGP region, whereas black arrows indicate unpredictable atmospheric noise. Green shading on the bottom left figure indicates the climatological annual cycle, and thick red line indicates enhancement of April mean MFC925 and cPrcp due to SST-forced atmospheric negative PNA pattern.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/8/eaaw9950/DC1

    Fig. S1. Schematics illustrating temporal and spatial scale interactions responsible for tornado generation in the SGP and the associated physical processes.

    Fig. S2. Seasonal cycle and year-to-year variability of the number of tornadoes over the SGP region.

    Fig. S3. Properties of the PNA teleconnection pattern.

    Fig. S4. Significance test for regression patterns.

    Fig. S5. Correlation coefficients between anomalies of tornado frequency, large-scale atmospheric variables, and mesoscale variables associated with tornadic supercell thunderstorms in each month from April to June.

    Fig. S6. Scatterplot of tornado frequencies for April and May for the period 1954–2010 as a function of CAPE.

    Fig. S7. Large-scale patterns controlling the tornado activity in the SGP in March and the seasonal cycle of the low-level jet.

    Fig. S8. Influence of PNA teleconnection on kinematic properties of the tornadic environment.

    Fig. S9. Mesoscale activity during strong and weak April tornado years.

    Table S1. Details of the AMIP models used in this study.

  • Supplementary Materials

    The PDF file includes:

    • Fig. S1. Schematics illustrating temporal and spatial scale interactions responsible for tornado generation in the SGP and the associated physical processes.
    • Fig. S2. Seasonal cycle and year-to-year variability of the number of tornadoes over the SGP region.
    • Fig. S3. Properties of the PNA teleconnection pattern.
    • Fig. S4. Significance test for regression patterns.
    • Fig. S5. Correlation coefficients between anomalies of tornado frequency, large-scale atmospheric variables, and mesoscale variables associated with tornadic supercell thunderstorms in each month from April to June.
    • Fig. S6. Scatterplot of tornado frequencies for April and May for the period 1954–2010 as a function of CAPE.
    • Fig. S7. Large-scale patterns controlling the tornado activity in the SGP in March and the seasonal cycle of the low-level jet.
    • Fig. S8. Influence of PNA teleconnection on kinematic properties of the tornadic environment.
    • Fig. S9. Mesoscale activity during strong and weak April tornado years.
    • Legend for table S1

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    Other Supplementary Material for this manuscript includes the following:

    • Table S1 (Microsoft Excel format). Details of the AMIP models used in this study.

    Files in this Data Supplement:

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