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Western North Pacific tropical cyclone activity modulated by phytoplankton feedback under global warming

Nature Climate Change (2024)Cite this article

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Abstract

The effects of bio-optical feedback through chlorophyll on future tropical cyclone (TC) activity are not well understood. Here we use Earth system model simulations with the biogeochemical feedback turned on and off to investigate the influence of chlorophyll changes on projections of TCs over the western North Pacific (WNP). An increase in chlorophyll in the tropical eastern Pacific and a decrease in the tropical western Pacific lead to a La Niña-like sea surface temperature warming. This pattern plays a crucial role in enhancing the genesis potential index over the southeastern WNP by 10.16% through strengthening of the Walker and local Hadley circulations. The enhanced genesis potential index is further supported by an additional higher-resolution atmospheric model experiment that shows a 71% increase in TC genesis over the southeastern WNP (from 2.00 to 3.43 yr−1) and a 27.02% enhancement in TC landfall frequency in East Asia (from 4.33 to 5.50 yr−1).

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Fig. 1: Biologically induced changes in future GPI and associated large-scale environmental conditions over the WNP.
Fig. 2: Large-scale tropical circulation and SST modified by future chlorophyll changes.
Fig. 3: Simulated future chlorophyll changes and impact on environmental variables.
Fig. 4: Spatial differences in TC activity simulated by a higher-resolution atmospheric model.

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Data availability

Key model output data used in this study are available via Zenodo at https://doi.org/10.5281/zenodo.10732620 (ref. 54). Complete datasets can be requested from J.-Y.P.

Code availability

The model codes for CM2.1, TOPAZ and AM4.0 are publicly available at https://www.gfdl.noaa.gov/modeling-systems-group-public-releases/. All figures were generated using Grid Analysis and Display System (GrADS) version 2.1.0, which can be downloaded from http://cola.gmu.edu/grads/downloads.php. The figure codes written in GrADS are available via Zenodo at https://doi.org/10.5281/zenodo.10732620 (ref. 54).

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) under grant numbers 2021R1I1A1A01040364 (H.-K.K.), RS-2023-00207866 (H.-K.K. and J.-Y.P.) and 2022R1A3B1077622 (J.-S.K.), as well as the Korea Meteorological Administration Research and Development Program under grant number KMI2022-01312 (J.-Y.P. and D.-S.R.P.).

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Authors and Affiliations

  1. Department of Earth and Environmental Sciences, Jeonbuk National University, Jeonju, South Korea

    Han-Kyoung Kim & Jong-Yeon Park

  2. Department of Environment and Energy, Jeonbuk National University, Jeonju, South Korea

    Jong-Yeon Park

  3. Department of Earth Science Education, Kyungpook National University, Daegu, South Korea

    Doo-Sun R. Park

  4. Weather Extremes Education and Research Team, Department of Atmospheric Sciences, Kyungpook National University, Daegu, South Korea

    Doo-Sun R. Park

  5. School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea

    Jong-Seong Kug

  6. Marine Science and Convergence Engineering, ERICA, Hanyang University, Ansan, South Korea

    Sang-Wook Yeh

  7. Center for Climate Physics, Institute for Basic Science (IBS), Busan, South Korea

    Jun-Hyeok Son

  8. Pusan National University, Busan, South Korea

    Jun-Hyeok Son

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Contributions

H.-K.K., J.-Y.P. and D.-S.R.P. conceptualized the research. H.-K.K. and J.-Y.P. conducted model runs. All authors interpreted the results and wrote the paper.

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Correspondence to Jong-Yeon Park or Doo-Sun R. Park.

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Extended data

Extended Data Fig. 1 Comparison of genesis potential index between observational data and Earth system model simulations.

a, The monthly variations of observed GPI and TC genesis frequency (yr−1), averaged over the WNP TC development region (100° E–180°, 5°–35° N) for the period from 1979 to 2016. b, same as a except for simulated GPI in a 250-year present climate with prescribed chlorophyll, BIO_on, and BIO_off. The observed GPI and TC genesis frequency are calculated using data from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis 1 (NCEP-NCAR R1) and Regional Specialized Meteorological Centers Tokyo-Typhoon Center (RSMC), respectively.

Extended Data Fig. 2 Comparison of each component of the genesis potential index between reanalysis data and Earth system model simulations.

Monthly variations in the four constituent terms of GPI derived from observational and model data: a-d, potential intensity (PI) (a), vorticity (b), humidity (c), and shear (d) terms. These variations are averaged over the WNP TC development region (100° E–180°, 5°–35° N). The observed terms are obtained from NCEP-NCAR R1 for the period from 1979 to 2016. The simulated terms are derived from BIO_on and BIO_off.

Extended Data Fig. 3 Spatial difference of four terms constituting genesis potential index.

a-d, Difference in spatial patterns of PI (m s−1) (a), RV850 (10−6 s−1) (b), RH700 (%) (c), and VWS (m s−1) (d) between BIO_on and BIO_off. Cross-hatching in each panel implies significantly different values satisfying at the 5 % level, determined using a two-tailed Student t-test. Maps generated using GrADS version 2.1.0 (http://cola.gmu.edu/grads/downloads.php).

Extended Data Fig. 4 Future projections of tropical Pacific sea surface temperature with biogeochemical feedback turned on and off.

a-b, The horizonal difference in tropical Pacific SST (°C) between the 30-year future and 250-year present climate simulations with activated (a) and inactivated (b) biogeochemical models during TC peak season (that is, JASO). Maps generated using GrADS version 2.1.0 (http://cola.gmu.edu/grads/downloads.php).

Extended Data Fig. 5 Future chlorophyll changes.

a, The horizontal distribution of surface chlorophyll (μg kg−1) in BIO_off. b, The horizontal difference of chlorophyll (μg kg−1) between BIO_on and BIO_off. Maps generated using GrADS version 2.1.0 (http://cola.gmu.edu/grads/downloads.php).

Extended Data Fig. 6 Nutrient limitations for chlorophyll in the tropical eastern Pacific.

a-b, Scatter plot of monthly mean surface iron (nmol kg−1) versus surface chlorophyll (μg kg−1) (a) and surface nitrate (μmol kg−1) versus surface chlorophyll (μg kg−1) (b) in BIO_on, averaged over the TEP (120°–90° W, 2.5° S–2.5° N). The mint dots denote the cases when the nitrate concentration is larger than its climatology in (a), and the iron concentration is larger than its climatology in (b). The black vertical line represents the climatological mean of surface iron and surface nitrate averaged over the TEP.

Extended Data Fig. 7 Simulated future nutrient changes and their associated physical conditions.

a-d, Difference of zonal-depth profile of iron (nmol kg−1) (a), nitrate (μmol kg−1) (b), zonal current (m s−1) (c), and vertical velocity (μm s−1) (d) between BIO_on and BIO_off. Contours in each panel represent the climatology of each variable in BIO_off.

Extended Data Fig. 8 Relationship between lifetime maximum 10 m wind speed and tropical cyclone lifetime.

LMW (m s−1) versus lifetime (days) of TCs over the WNP in BIO_on_AM. The black vertical line represents an LMW of 34 m s−1, indicating the wind speed of a typhoon category. Red dots denote the TCs generated in the SE-WNP.

Extended Data Fig. 9 Comparison of genesis and track densities between observation and atmospheric model simulation.

a-b, Genesis density (5° × 5°, yr−1) of observed (a) and simulated (b) TCs during peak GPI season. (c) and (d) are the same as (a) and (b) except for track density (5° × 5°, yr−1), respectively. The observed TCs are obtained from RSMC data for the period from 1979 to 2016. The simulated TCs are detected from a 12-year-long simulation using AM4.0 prescribed with monthly climatological Hadley Centre SST and sea ice data for the period from 1979 to 2016. The annual mean TC genesis frequency is shown in the upper-right corner in (a) and (b). Maps generated using GrADS version 2.1.0 (http://cola.gmu.edu/grads/downloads.php).

Extended Data Fig. 10 Comparison of the probability of lifetime maximum 10 m wind speed of tropical cyclones between observation and atmospheric model simulation.

Histograms of probability distributions of LMW for observed TCs and detected TCs in the atmospheric model simulation. Each bar represents a 5 m s−1 interval in wind speed.

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Kim, HK., Park, JY., Park, DS.R. et al. Western North Pacific tropical cyclone activity modulated by phytoplankton feedback under global warming. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-01976-6

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