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Increased typhoon activity in the Pacific deep tropics driven by Little Ice Age circulation changes

Nature Geoscience volume 13, pages 806–811 (2020)Cite this article

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Abstract

The instrumental record reveals that tropical cyclone activity is sensitive to oceanic and atmospheric variability on inter-annual and decadal scales. However, our understanding of the influence of climate on tropical cyclone behaviour is restricted by the short historical record and the sparseness of prehistorical reconstructions, particularly in the western North Pacific, where coastal communities suffer loss of life and livelihood from typhoons annually. Here, to explore past regional typhoon dynamics, we reconstruct three millennia of deep tropical North Pacific cyclogenesis. Combined with existing records, our reconstruction demonstrates that low-baseline typhoon activity prior to 1350 ce was followed by an interval of frequent storms during the Little Ice Age. This pattern, concurrent with hydroclimate proxy variability, suggests a centennial-scale link between Pacific hydroclimate and tropical cyclone climatology. An ensemble of global climate models demonstrates a migration of the Pacific Walker circulation and variability in two Pacific climate modes during the Little Ice Age, which probably contributed to enhanced tropical cyclone activity in the tropical western North Pacific. In the next century, projected changes to the Pacific Walker circulation and expansion of the tropics will invert these Little Ice Age hydroclimate trends, potentially reducing typhoon activity in the deep tropical Pacific.

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Fig. 1: Western Pacific tropical cyclone reconstructions.
Fig. 2: Comparison of our storm reconstruction with Pacific paleoclimate proxies.
Fig. 3: Change in tropical cyclogenesis potential from the MCA (1000–1300 ce) to the LIA (1400–1700 ce).
Fig. 4: Relationship between vertical wind shear anomalies and the Pacific Walker circulation.
Fig. 5: Ensemble median correlation between annual mean vertical wind shear and the PMM wind index in GCM ensemble results.

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Data availability

Grain-size data, median ages by depth, centennial event frequency and the dated material for core LTD3 are available from the National Oceanic and Atmospheric Administration National Centers for Environmental Information (NCEI) paleoclimatology database https://www.ncdc.noaa.gov/paleo/study/31132 and can also be found on the Woods Hole Open Access Server (WHOAS), https://doi.org/10.26025/1912/26159. Much of the data from the existing literature plotted in Fig. 2 can be found on the NCEI paleoclimatology database (https://www.ncdc.noaa.gov/paleo-search/) using the following data set ids: Fig. 2a [noaa-recon-13684], Fig. 2b [noaa-coral-13672], Fig. 2c [noaa-lake-29432], Fig. 2d [noaa-icecore-14174], Fig. 2e [noaa-cave-20530]. Data for Fig. 1b,c are available as tables at https://doi.org/10.1016/j.quascirev.2013.07.01921 and in the supplementary information at https://doi.org/10.1002/2015PA00287020.

Code availability

The MATLAB code used to analyse the GCM output and the code and data used to plot the figures are available on the Woods Hole Open Access Server (WHOAS), https://doi.org/10.26025/1912/26159.

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Acknowledgements

We thank student intern D. Carter for extensive labwork on core LTD3. This work was supported by the Strategic Environmental Research and Development Program (SERDP RC-2336). C.C.U. acknowledges support from NSF under AGS-1602455. We acknowledge the WCRP’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. CMIP5 model output was provided by the WHOI CMIP5 Community Storage Server via their website: http://cmip5.whoi.edu/. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Author information

Authors and Affiliations

  1. Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    James F. Bramante, Andrew D. Ashton, Richard M. Sullivan & Jeffrey P. Donnelly

  2. Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    James F. Bramante

  3. School of Environment, The University of Auckland, Auckland, New Zealand

    Murray R. Ford

  4. Department of Earth Sciences, Simon Fraser University, Burnaby, British Columbia, Canada

    Paul S. Kench

  5. US Geological Survey, Florence Bascom Geoscience Center, Reston, VA, USA

    Michael R. Toomey

  6. Department of Atmospheric and Ocean Sciences, University of Colorado, Boulder, CO, USA

    Kristopher B. Karnauskas

  7. Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Caroline C. Ummenhofer

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Contributions

J.F.B. performed the labwork and model analysis, and wrote the initial draft. J.F.B., M.R.F., P.S.K., A.D.A., M.R.T., R.M.S. and J.P.D. performed the fieldwork and advised on sedimentology analysis and interpretation. K.B.K. and C.C.U. advised on model analysis and interpretation. All authors discussed the results, commented on the manuscript and contributed revisions.

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Correspondence to James F. Bramante.

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Extended data

Extended Data Fig. 1 Maps of the reconstruction site.

Maps of the reconstruction site: a, Map of the tropical western Pacific, with the storm tracks of every tropical cyclone in the IBTrACS dataset to pass within 100 km of Jaluit Atoll; b, map of the site showing location of cores as green asterisks; c, bathymetric profile of the basin from which the sediment cores were extracted, with a salinity profile; d, map of the tropical Pacific with locations of the reconstructions referenced in this study.

Extended Data Fig. 2 Profiles of sediment cores collected from the Jaluit Atoll blue hole.

Profiles of sediment cores collected from the Jaluit Atoll blue hole. a, BACON age model for LTD3 and b, coarse fraction profiles of LTD2 and LTD3. BACON-calibrated radiocarbon dates are displayed as green triangles. A tie point used in the age models and to establish core top depth by comparing drives are indicated with a blue dashed line. Storm beds were identified as those samples that exceeded 1.5 standard deviations above an 11-cm moving average, where both statistics were calculated while ignoring >2 standard deviation outliers.

Extended Data Fig. 3 Illustration of the method used to calculate centennial event frequency from coarse fraction.

Illustration of the method used to calculate centennial event frequency from coarse fraction. c, Coarse fraction anomaly is used to identify event deposits as in Methods. b, The annually-binned probability distribution function (PDF, blue shading) of each event deposit is extracted from the age model and summed for each year (black line). a, The sum of annual PDFs is summed over a 100-year moving window to construct a time series of centennial event frequency incorporating age model uncertainty.

Extended Data Fig. 4 Sensitivity analysis of the procedure used to identify event beds in Jaluit Atoll core LTD3 grain size data, using the 250–2000 μm coarse fraction.

Sensitivity analysis of the procedure used to identify event beds in Jaluit Atoll core, LTD3 grain size data, using the 250–2000 μm coarse fraction. Coarse fraction variance over the entire core was calculated a, with a moving-average window-size of 11 cm and exclusion of outliers, b, with a moving average window size of 11 cm and inclusion of outliers, c, with a moving average window size of 31 cm and exclusion of outliers, and d, with a moving average window size of 31 cm and inclusion of outliers. For each of these four cases, event beds were flagged with 1.5 standard deviation and 2 standard deviation cutoffs. The active interval thresholds for each of these cutoffs represents the 97.5 percentile frequency for a Poisson distribution with the core’s mean event frequency. Active intervals were identified as intervals lasting at least a century in which those thresholds were exceeded. Passive intervals were identified as intervals with zero events that were less than 2.5% likely to occur according to a gamma distribution. P-values are the cumulative frequency distribution values for a gamma distribution defined by a Poisson process defined by the cores centennial event frequency and the number of events contained in an active or passive interval, evaluated for the length of time between the first and last event in that interval.

Extended Data Fig. 5 Same as ED Fig. 4, but using the >250 μm coarse fraction.

Same as Extended Data Fig. 4, but using the >250 μm coarse fraction.

Extended Data Fig. 6 Ensemble median relative anomaly in tropical cyclone genesis indexes during the Little Ice Age (1400–1700 CE).

Ensemble median relative anomaly in tropical cyclone genesis indexes during the LIA (1400–1700 CE). Relative anomaly was calculated as Δ = (LIA - MCA) / MCA × 100%. The a, Genesis Potential Index41 is calculated from four variables: b, low level vorticity, η (s−1), c, vertical wind shear (ms−1), d, potential intensity (ms−1), and e, the mid-troposphere saturation deficit, χ (dimensionless)57. The colour palettes are aligned so red always indicates increasing cyclogenesis potential. The sign of relative vorticity in the southern hemisphere in b) was reversed so positive change indicates more cyclonic vorticity. Percent change values were calculated from storm season averages for the two time periods. In the northern hemisphere, the WNP storm season (JASON) was used. No data is shown for 1°S-1°N to indicate the different months used for averaging in each hemisphere. Black stippling indicates grid cells in which at least five of seven models agreed on the direction of change. The green symbols represent the locations of storm reconstructions (Extended Data Fig. 1).

Extended Data Fig. 7 Spearman rank correlation between mean storm season vertical wind shear and Pacific Meridional Mode for each of the CMIP5 models.

Spearman rank correlation between mean storm season vertical wind shear and Pacific Meridional Mode for each of the CMIP5 models. Correlation coefficients were calculated for Last Millennium experiment results for the period 1000–1850 CE. Black stippling indicates statistical significance as determined by a two-tailed Student t-test after taking into account multiple hypothesis testing using the false discovery rate procedure and setting q = 2.5%61.

Extended Data Fig. 8 Hadley circulation anomalies during the Little Ice Age (1400–1700 CE).

Hadley circulation anomalies during the Little Ice Age (1400–1700 CE). Zonal (100–180°E) mean vertical pressure velocity associated with meridional overturning circulation (shading, vectors) and non-divergent meridional wind velocity (vectors) a,b, averaged over 1000–1850 CE and c,d, the LIA (1400–1700 CE) anomaly relative to 1000–1850 CE. The dashed vertical lines indicate the equator. Negative (positive) vertical pressure velocity values indicate ascending (descending) motion. Black stippling in c,d) indicates pressure/latitude coordinates where at least 5 of the 7 models agreed on the direction of change.

Extended Data Fig. 9

Sediment core radiocarbon dates.

Extended Data Fig. 10

Summary of CMIP5 models from which monthly mean data were used in the last millennium analysis.

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Bramante, J.F., Ford, M.R., Kench, P.S. et al. Increased typhoon activity in the Pacific deep tropics driven by Little Ice Age circulation changes. Nat. Geosci. 13, 806–811 (2020). https://doi.org/10.1038/s41561-020-00656-2

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