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Trophic level decoupling drives future changes in phytoplankton bloom phenology

Nature Climate Change volume 12, pages 469–476 (2022)Cite this article

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Abstract

Climate change can drive shifts in the seasonality of marine productivity, with consequences for the marine food web. However, these alterations in phytoplankton bloom phenology (initiation and peak timing), and the underlying drivers, are not well understood. Here, using a 30-member Large Ensemble of climate change projections, we show earlier bloom initiation in most ocean regions, yet changes in bloom peak timing vary widely by region. Shifts in both initiation and peak timing are induced by a subtle decoupling between altered phytoplankton growth and zooplankton predation, with increased zooplankton predation (top-down control) playing an important role in altered bloom peak timing over much of the global ocean. Only in limited regions is light limitation a primary control for bloom initiation changes. In the extratropics, climate-change-induced phenological shifts will exceed background natural variability by the end of the twenty-first century, which may impact energy flow in the marine food webs.

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Fig. 1: Projected future changes in the phytoplankton bloom phenology and emergence timescales.
Fig. 2: Mechanistic attribution of the bloom phenological changes for the subpolar North Atlantic (55° N, 45° W).
Fig. 3: Dominant contribution(s) to shifts in bloom initiation and bloom peak timing.
Fig. 4: Environmental drivers that cause the future trophic level decoupling.

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Data availability

The 30-member GFDL-ESM2M ensemble simulations53 used in this study are available through the data transfer service Globus (https://www.globus.org). In the server (http://poseidon.princeton.edu), daily surface ocean variables are available under: /GFDL_ESM2M/ENSEMBLE_RCP85/OCN/OCN_1D_1×1/ and monthly mean ocean fields are under /GFDL_ESM2M/ENSEMBLE_RCP85/OCN/OCN_1M_1×1. The MODIS-Aqua Level-3 Binned Chlorophyll Data50 are available at https://oceancolor.gsfc.nasa.gov.

Code availability

The codes used to analyse data and generate all figures are based on Python with associated standard Python packages (Xarray, NumPy, SciPy, Matplotlib, Cartopy and so on). The codes54 are available from https://doi.org/10.5281/zenodo.6301884.

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Acknowledgements

R.Y., K.B.R., K.S. and A.T. were supported by the Institute for Basic Science (IBS), Republic of Korea, under IBS-R028-D1. S.S. acknowledges support from the National Science Foundation’s Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project under the NSF Award PLR-1425989, with additional support from the National Oceanic and Atmospheric Administration (NOAA) and NASA Award NNX17AI75G. D.B. acknowledges support from NOAA under Ecosystem and Harmful Algal Bloom (ECOHAB) Award NA18NOS4780174. R.D.S. acknowledges support from NOAA (NA18OAR4320123). We thank H.-G. Lim for constructive suggestions on the manuscript. ESM2M output analysis was conducted on the IBS/ICCP supercomputer ‘Aleph’, 1.43 peta flops high-performance Cray XC50-LC Skylake computing system with 18,720 processor cores, 9.59 PB storage and 43 PB tape archive space. High Performance Computing resources for the ESM2M Large Ensemble simulations themselves were provided by NOAA Oceanic and Atmospheric Research/Geophysical Fluid Dynamics Laboratory.

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Author notes

  1. Ryohei Yamaguchi

    Present address: Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan

Authors and Affiliations

  1. Center for Climate Physics, Institute for Basic Science, Busan, South Korea

    Ryohei Yamaguchi, Keith B. Rodgers, Axel Timmermann & Karl Stein

  2. Pusan National University, Busan, South Korea

    Ryohei Yamaguchi, Keith B. Rodgers, Axel Timmermann & Karl Stein

  3. AOS Program, Princeton University, Princeton, NJ, USA

    Sarah Schlunegger & Richard D. Slater

  4. Department of Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, CA, USA

    Daniele Bianchi

  5. NOAA/OAR Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA

    John P. Dunne

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Contributions

R.Y. and K.B.R. conceptualized the scientific framing of this study. R.Y. conducted all the analyses and wrote the initial draft of the manuscript. R.Y., K.B.R., A.T., K.S., S.S., D.B. and J.P.D. helped develop the scientific ideas and analytical methods, interpret the results and write the final draft of the manuscript. The ESM2M simulations were performed by S.S. and R.D.S., and postprocessing was provided by K.B.R.

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Correspondence to Ryohei Yamaguchi.

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Nature Climate Change thanks Lionel Arteaga, Emmanuel Boss, Stephanie Henson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Biome map and biome-aggregated time series of observed and projected bloom initiation and peak timing.

In order from the pole, each basin (North (N) and South (S) Pacific (P), Atlantic (A), and Indian Ocean(I)) except for the North Indian Ocean has ice (ICE), subpolar seasonally stratified (SPSS), subtropical seasonally stratified (STSS), subtropical permanently stratified (STPS), and equatorial (EQU) biomes. Observational time series are computed from satellite-derived chlorophyll product (MODIS-Aqua) and the projections are simulated by the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2M) under Historical/RCP8.5 scenario. All time series are anomalies relative to satellite observational period means (2003-2019). The grey lines mark differences between bloom peak timing anomalies and initiation anomalies, indicating changes in the net growth period length. The shadings around the green and orange lines represent the two standard deviation range across the 30 ensemble members of ESM2M. Time of Emergence for the biome-aggregated changes in bloom initiation and peak timing is shown in green and orange vertical lines, respectively. If there is no corresponding vertical line in the panel, the phenological change will not emerge by the end of the simulation period. Note that the model time series are created without the resampling process (see Methods) in order to be consistent along the time-dimension of the model output from year 1990 to the end of the simulation.

Extended Data Fig. 2 Projected future changes in the phytoplankton bloom magnitude.

a, Ensemble mean climatology (1990–2020) of the phytoplankton bloom magnitude. b, Ensemble mean trends (1990–2100 under Historical/RCP8.5 scenario) of the bloom magnitude, simulated by the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2M). Regions where the trend is statistically insignificant (the 99% confidence level based on a t-test) are stippled. Black contours superimposed on the maps denote the biome boundaries used in this study. c, Biome-averaged changes (2080–2100 minus 1990–2010) in the bloom magnitude. In each ocean basin, ice (ICE), subpolar seasonally stratified (SPSS), subtropical seasonally stratified (STSS), subtropical permanently stratified (STPS), and equatorial (EQU) biomes are assigned in order from the pole (spatial map in Extend Data Fig. 1). Ensemble mean changes are shown as bars, and dots on each bar represent individual members’ changes. The stars at the end of bars indicate that the ensemble mean changes will emerge, that is, the climate-forced changes exceed the background internal variability, by the end of the 21st century. Note that, in panel c, another y-axis is provided on the right only for scaling the Northern Hemisphere ICE result.

Extended Data Fig. 3 Mechanistic attribution of the bloom phenological changes for the subarctic North Pacific (150°W, 50°N).

a, Future (2080–2100) and present-day (1990–2010) annual cycles of surface chlorophyll concentration (Chl, g kg-1), b, chlorophyll accumulation rate (r ≡ dlnChl/dt, day-1) and its present-day term balance. r is determined by phytoplankton growth (μ) and loss rate (l), and temporal changes in Chl to Carbon ratio (θ) and mixed layer depth (h) (equation 1). c, Budget for the accumulation rate changes (Δr, future r minus present-day r). The bars with respect to the left axis are accumulation rate change and three changes that drives the Δr (equation 6). Changes in the growth and loss rates are also shown separately as lines with shading (right axis). d, Decomposition of the growth rate changes into changes in environmental drivers. The phytoplankton growth rate change is decomposed into changes in Temperature-, Nutrient-, Light-limitation (equation 8). e, Decomposition of the loss rate changes into changes in environmental drivers. The phytoplankton loss rate change is decomposed into changes in Temperature limitation and phytoplankton abundance (equation 9). The periods commonly shaded in light grey in a-d represent the periods between the present-day and future bloom initiation/peak timing determined from the annual cycle of r (b). All line shadings are the range of two standard deviations across the 30 ensemble members.

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Supplementary Notes 1 and 2, Figs. 1–9 and Table 1.

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Yamaguchi, R., Rodgers, K.B., Timmermann, A. et al. Trophic level decoupling drives future changes in phytoplankton bloom phenology. Nat. Clim. Chang. 12, 469–476 (2022). https://doi.org/10.1038/s41558-022-01353-1

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