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Abrupt Southern Great Plains thunderstorm shifts linked to glacial climate variability

Nature Geoscience volume 14, pages 396–401 (2021)Cite this article

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Abstract

Thunderstorms in the Southern Great Plains of the United States are among the strongest on Earth and have been shown to be increasing in intensity and frequency during recent years. Assessing changes in storm characteristics under different climate scenarios, however, remains highly uncertain due to limitations in climate model physics. We analyse oxygen isotopes from Texas stalactites from 30–50 thousand years ago to assess past changes in thunderstorm size and duration using a modern radar-based calibration for the region. Storm regimes shift from weakly to strongly organized on millennial timescales and are coincident with well-known abrupt climate shifts during the last glacial period. Modern-day synoptic analysis suggests that thunderstorm organization in the Southern Great Plains is strongly coupled to changes in large-scale wind and moisture patterns. These changes in the large-scale circulation may be used to assess future predictions and palaeo-simulations of mid-latitude thunderstorm climatologies.

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Fig. 1: Mesoscale convective systems and central Texas hydroclimate.
Fig. 2: Distribution of water oxygen isotopes in SGP storms.
Fig. 3: Central Texas stalactite time series in the context of MIS3 palaeoclimate.
Fig. 4: Synoptic mechanisms possibly underlying SGP thunderstorm variability.

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Data availability

All speleothem isotopic data measured in this study will be archived in the Paleoclimatology Dataset repository in the National Centers for Environmental Information, NOAA database (https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets). All rainfall isotope data will be publicly archived in the Waterisotopes Database (http://wateriso.utah.edu/waterisotopes/index.html).

Code availability

The Jupyter Notebooks use public, open-source packages to perform all Python calculations and generate all figures herein, and are available upon request to the corresponding author.

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Acknowledgements

We acknowledge T. Bhattacharya, S. B. Malevich, G. Boyd and the Williamson County Conservation Foundation. We are grateful for constructive feedback from N. Levin. We thank Y.-C. Chou for conducting U/Th dating in the High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University. Funding was provided in part by a Texas A&M University high-impact undergraduate research grant. K.T. acknowledges support from the University of Arizona Technology and Research Initiative Fund (TRIF). U/Th dating was supported by the Science Vanguard Research Program of the Ministry of Science and Technology (108-2119-M-002-012) and the Higher Education Sprout Project of the Ministry of Education, Taiwan ROC (108L901001).

Author information

Authors and Affiliations

  1. Department of Geography, Texas A&M University, College Station, TX, USA

    Christopher R. Maupin & E. Brendan Roark

  2. Department of Geosciences, University of Arizona, Tuscon, AZ, USA

    Kaustubh Thirumalai

  3. High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University, Taipei, Taiwan ROC

    Chuan-Chou Shen, Oruç Baykara & Tsai-Luen Yu

  4. Research Center for Future Earth, National Taiwan University, Taipei, Taiwan ROC

    Chuan-Chou Shen & Tsai-Luen Yu

  5. Global Change Research Center, National Taiwan University, Taipei, Taiwan ROC

    Chuan-Chou Shen

  6. Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA

    Courtney Schumacher

  7. SWCA Environmental Consultants, Austin, TX, USA

    Stephen Van Kampen-Lewis

  8. Environmental Programs in Geosciences, Texas A&M University, College Station, TX, USA

    Audrey L. Housson & C. Lorraine McChesney

  9. Department of Geography, Pamukkale University, Denizli, Turkey

    Oruç Baykara

  10. Cambrian Environmental, Austin, TX, USA

    Kemble White IV

  11. Institute for Geophysics, The University of Texas at Austin, Austin, TX, USA

    Judson W. Partin

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Contributions

C.R.M. and E.B.R. developed the concept. C.R.M., E.B.R., C.S., K.T., J.W.P. and C.-C.S. analysed results. E.B.R. acquired undergraduate research funding. C.R.M., A.L.H. and C.L.M. micromilled stalactite subsamples, and performed water and carbonate stable isotope measurements; O.B., Y.-C.C., T.-L.Y. and C.-C.S. subsampled and measured U and Th compositions; K.W. and S.V.K.-L. collected sample stalactites; C.R.M. and S.V.K.-L. collected daily rainfall samples. C.R.M. and J.W.P. developed the approach for screening high-232Th; C.R.M. and C.S. developed the storm categorization scheme; E.B.R., T.-L.Y. and C.-C.S. administered laboratory, personnel and instrumental resources; K.W. and S.V.K.-L. supervised sample collection, surveys and environmental impact assessments. C.R.M. wrote or implemented code for analysing investigation results with assistance from K.T.; C.R.M. and K.T. designed visualizations for investigation results. C.R.M. wrote the original draft. All authors contributed to review and editing of submitted manuscript.

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Correspondence to Christopher R. Maupin.

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The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Naomi Levin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super. 

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Extended data

Extended Data Fig. 1 Age Model for stalactite 14FM2243-C (depicted with both U/Th and stable isotope subsample transects).

a, Calculated U/Th ages, relative to the date of subsample preparation for U and Th analyses, in thousands of years (ky) before 1950 CE (BP), plotted as burnt orange closed circles versus stalactite depth. Depth refers to distance from stalactite tip, increasing towards the cave ceiling; zero mm is the youngest calcite subsampled for δ18Oc. Error bars depict ± 2σ analytical uncertainty of U/Th analyses. The line and shaded envelope shows the median, and 10th to 90th percentile distributions of our age model, respectively; estimated by fitting N = 1,000 splines in the Bchron software package4. b, 232Th versus depth, exhibiting consistent, low32part-per-billion concentrations irrespective of sample depth. c, A histogram and Gaussian KDE of 232Th concentrations highlight the narrow distribution of232Th values.

Extended Data Fig. 2 Age Model for stalactite 13RM620-B (depicted with both U/Th and stable isotope subsample transects).

a, As in Extended Data Fig. 1a, except the youngest calcite subsampled for δ18Oc begins 40.25 mm from the tip; a hiatus immediately above this depth results in an abrupt transition into higher-232Th calcite (not shown). Dates plotted in blue were excluded from age model construction due to high 232Th concentrations, indicating relatively abundant non-radiogenic thorium and requiring larger age corrections with uncertain initial 230Th/232Th values. b,c, As in Extended Data Fig. 1b and c. The consistent, part per billion concentrations of included ages can be contrasted with the variable, higher-232Th character of excluded ages irrespective of depth.

Extended Data Fig. 3 Cross-sectional view representative of sample-void overburdens.

Note the shallow Edwards Limestone epikarst ( < 3 m), highlighted by the white bracket.

Extended Data Fig. 4 Overhead perspective of karst-void ceiling breached during trenching.

Note abundant fracture sets (white arrows), permitting recharge from any rainfall sufficient to exceed surface alluvium storage capacity and evapotranspiration. Voids breached and surveyed maintained 100% relative humidity and actively dripping groundwater infiltration.

Extended Data Fig. 5 Fastest and slowest growth axis δ18Oc in stalactite 14FM2243-C.

The reproducibility indicates kinetic effect concerns are not warranted for interpretations of sample calcite δ18O.

Extended Data Fig. 6 Mean annual streamfunction and mean annual temperature correlations, 1981-2018.

Monthly point-correlations between central Texas mean annual 10 m air temperature5, global gridded 10 m mean annual air temperature (over land)5, and global gridded mean annual SST6 (P < 10−3 diagonally hatched). Streamfunction here is calculated from the mean annual 10 m U and V wind components5. The yellow triangle denotes our central Texas study site, while the red circles locate Gulf of Mexico MIS3 SST reconstructions7,8.

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Maupin, C.R., Roark, E.B., Thirumalai, K. et al. Abrupt Southern Great Plains thunderstorm shifts linked to glacial climate variability. Nat. Geosci. 14, 396–401 (2021). https://doi.org/10.1038/s41561-021-00729-w

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