Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Trophic level decoupling drives future changes in phytoplankton bloom phenology


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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

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.

Data availability

The 30-member GFDL-ESM2M ensemble simulations53 used in this study are available through the data transfer service Globus ( In the server (, 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

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


  1. Bindoff, N. L. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 447–587 (Cambridge Univ. Press,2019).

  2. Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

    Article  Google Scholar 

  3. Fu, W., Randerson, J. T. & Moore, J. K. Climate Change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences 13, 5151–5170 (2016).

    Article  Google Scholar 

  4. Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).

    Article  CAS  Google Scholar 

  5. Cushing, D. H. in Advances in Marine Biology Vol. 26 (eds Blaxter, J. H. S. & Southward, A. J.) 249–293 (Academic Press, 1990).

  6. Durant, J., Hjermann, D., Ottersen, G. & Stenseth, N. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. 33, 271–283 (2007).

    Article  Google Scholar 

  7. Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).

    Article  CAS  Google Scholar 

  8. Durant, J. M. et al. Contrasting effects of rising temperatures on trophic interactions in marine ecosystems. Sci. Rep. 9, 15213 (2019).

    Article  CAS  Google Scholar 

  9. Penuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).

    Article  CAS  Google Scholar 

  10. Ruosteenoja, K., Markkanen, T. & Räisänen, J. Thermal seasons in northern Europe in projected future climate. Int. J. Climatol. 40, 4444–4462 (2020).

    Article  Google Scholar 

  11. Jeong, S.-J., Ho, C.-H., Gim, H.-J. & Brown, M. E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Change Biol. 17, 2385–2399 (2011).

    Article  Google Scholar 

  12. Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 035004 (2018).

    Article  CAS  Google Scholar 

  13. Chen, M., Melaas, E. K., Gray, J. M., Friedl, M. A. & Richardson, A. D. A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios. Glob. Change Biol. 22, 3675–3688 (2016).

    Article  Google Scholar 

  14. Kahru, M., Brotas, V., Manzano-Sarabia, M. & Mitchell, B. G. Are phytoplankton blooms occurring earlier in the Arctic?. Glob. Change Biol. 17, 1733–1739 (2011).

    Article  Google Scholar 

  15. Marchese, C. et al. Changes in phytoplankton bloom phenology over the North Water (NOW) polynya: a response to changing environmental conditions. Polar Biol. 40, 1721–1737 (2017).

    Article  Google Scholar 

  16. Friedland, K. D. et al. Phenology and time series trends of the dominant seasonal phytoplankton bloom across global scales. Glob. Ecol. Biogeogr. 27, 551–569 (2018).

    Article  Google Scholar 

  17. Winder, M. & Sommer, U. Phytoplankton response to a changing climate. Hydrobiologia 698, 5–16 (2012).

    Article  Google Scholar 

  18. Asch, R. G., Stock, C. A. & Sarmiento, J. L. Climate change impacts on mismatches between phytoplankton blooms and fish spawning phenology. Glob. Change Biol. 25, 2544–2559 (2019).

    Article  Google Scholar 

  19. Henson, S., Cole, H., Beaulieu, C. & Yool, A. The impact of global warming on seasonality of ocean primary production. Biogeosciences 10, 4357–4369 (2013).

    Article  Google Scholar 

  20. Henson, S. A., Cole, H. S., Hopkins, J., Martin, A. P. & Yool, A. Detection of climate change-driven trends in phytoplankton phenology. Glob. Change Biol. 24, e101–e111 (2018).

    Article  Google Scholar 

  21. Hashioka, T., Sakamoto, T. T. & Yamanaka, Y. Potential impact of global warming on North Pacific spring blooms projected by an eddy-permitting 3-D ocean ecosystem model. Geophys. Res. Lett. 36, L20604 (2009).

  22. Behrenfeld, M. J. & Boss, E. S. Resurrecting the ecological underpinnings of ocean plankton blooms. Annu. Rev. Mar. Sci. 6, 167–194 (2014).

    Article  Google Scholar 

  23. Behrenfeld, M. J. Climate-mediated dance of the plankton. Nat. Clim. Change 4, 880–887 (2014).

    Article  Google Scholar 

  24. Schlunegger, S. et al. Time of Emergence and Large Ensemble intercomparison for ocean biogeochemical trends. Glob. Biogeochem. Cycles 34, e2019GB006453 (2020).

  25. Sverdrup, H. U. On conditions for the vernal blooming of phytoplankton. ICES J. Mar. Sci. 18, 287–295 (1953).

    Article  Google Scholar 

  26. Sommer, U. & Lewandowska, A. Climate change and the phytoplankton spring bloom: warming and overwintering zooplankton have similar effects on phytoplankton. Glob. Change Biol. 17, 154–162 (2011).

    Article  Google Scholar 

  27. Behrenfeld, M. J. Abandoning Sverdrup’s critical depth hypothesis on phytoplankton blooms. Ecology 91, 977–989 (2010).

    Article  Google Scholar 

  28. Behrenfeld, M. J., Doney, S. C., Lima, I., Boss, E. S. & Siegel, D. A. Annual cycles of ecological disturbance and recovery underlying the subarctic Atlantic spring plankton bloom. Glob. Biogeochem. Cycles 27, 526–540 (2013).

    Article  CAS  Google Scholar 

  29. Arteaga, L. A., Boss, E., Behrenfeld, M. J., Westberry, T. K. & Sarmiento, J. L. Seasonal modulation of phytoplankton biomass in the Southern Ocean. Nat. Commun. 11, 5364 (2020).

    Article  CAS  Google Scholar 

  30. Behrenfeld, M. J. & Boss, E. S. Student’s tutorial on bloom hypotheses in the context of phytoplankton annual cycles. Glob. Change Biol. 24, 55–77 (2018).

    Article  Google Scholar 

  31. Schlunegger, S. et al. Emergence of anthropogenic signals in the ocean carbon cycle. Nat. Clim. Change 9, 719–725 (2019).

    Article  CAS  Google Scholar 

  32. Dunne, J. P. et al. GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part II: Carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).

    Article  Google Scholar 

  33. Dunne, J. P. et al. GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).

    Article  Google Scholar 

  34. Rodgers, K. B., Lin, J. & Frölicher, T. L. Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model. Biogeosciences 12, 3301–3320 (2015).

    Article  Google Scholar 

  35. Cabré, A., Shields, D., Marinov, I. & Kostadinov, T. S. Phenology of size-partitioned phytoplankton carbon-biomass from ocean color remote sensing and CMIP5 models. Front. Mar. Sci. 3, 39 (2016).

  36. Ji, R., Edwards, M., Mackas, D. L., Runge, J. A. & Thomas, A. C. Marine plankton phenology and life history in a changing climate: current research and future directions. J. Plankton Res. 32, 1355–1368 (2010).

    Article  Google Scholar 

  37. Henson, S. A., Beaulieu, C. & Lampitt, R. Observing climate change trends in ocean biogeochemistry: when and where. Glob. Change Biol. 22, 1561–1571 (2016).

    Article  Google Scholar 

  38. Hegerl, G. C. et al. Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. J. Clim. 9, 2281–2306 (1996).

    Article  Google Scholar 

  39. Massonnet, F. et al. Constraining projections of summer Arctic sea ice. Cryosphere 6, 1383–1394 (2012).

    Article  Google Scholar 

  40. Chemke, R., Zanna, L. & Polvani, L. M. Identifying a human signal in the North Atlantic warming hole. Nat. Commun. 11, 1540 (2020).

    Article  CAS  Google Scholar 

  41. Behrenfeld, M. J. et al. Revaluating ocean warming impacts on global phytoplankton. Nat. Clim. Change 6, 323–330 (2016).

    Article  Google Scholar 

  42. Geider, R., MacIntyre, H. & Kana, T. Dynamic model of phytoplankton growth and acclimation: responses of the balanced growth rate and the chlorophyll a:carbon ratio to light, nutrient-limitation and temperature. Mar. Ecol. Prog. Ser. 148, 187–200 (1997).

    Article  Google Scholar 

  43. Wassmann, P. & Reigstad, M. Future Arctic Ocean seasonal ice zones and implications for pelagic–benthic coupling. Oceanography 24, 220–231 (2011).

  44. Laufkötter, C. et al. Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences 12, 6955–6984 (2015).

    Article  Google Scholar 

  45. Nakamura, Y. & Oka, A. CMIP5 model analysis of future changes in ocean net primary production focusing on differences among individual oceans and models. J. Oceanogr. 75, 441–462 (2019).

    Article  Google Scholar 

  46. Marinov, I., Doney, S. C. & Lima, I. D. Response of ocean phytoplankton community structure to climate change over the 21st century: partitioning the effects of nutrients, temperature and light. Biogeosciences 7, 3941–3959 (2010).

    Article  Google Scholar 

  47. Henson, S. A., Cael, B. B., Allen, S. R. & Dutkiewicz, S. Future phytoplankton diversity in a changing climate. Nat. Commun. 12, 5372 (2021).

    Article  CAS  Google Scholar 

  48. Elson, P. et al. SciTools/cartopy: v0.20.2. Zenodo (2022).

  49. Frölicher, T. L. et al. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J. Clim. 28, 862–886 (2015).

    Article  Google Scholar 

  50. MODIS-Aqua Level 3 Binned Chlorophyll Data Version R2018.0 (NASA Ocean Biology Processing Group, 2017);

  51. Fay, A. R. & McKinley, G. A. Global open-ocean biomes: mean and temporal variability. Earth Syst. Sci. Data 6, 273–284 (2014).

    Article  Google Scholar 

  52. Behrenfeld, M. J., Doney, S. C., Lima, I., Boss, E. S. & Siegel, D. A. Reply to a comment by Stephen M. Chiswell on: ‘Annual cycles of ecological disturbance and recovery underlying the subarctic Atlantic spring plankton bloom’ by M. J. Behrenfeld et al. (2013). Global Biogeochem. Cycles 27, 1294–1296 (2013).

  53. Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2M) 30-Member Ensemble Simulations, Historical (1950–2005) and RCP8.5 (2006–2010) Scenario (NOAA GFDL, 2019);

  54. Yamaguchi, R. Notebooks for ‘Trophic level decoupling drives future change in phytoplankton bloom phenology’ by Yamaguchi et al. (2022). Zenodo (2022).

Download references


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.

Author information

Authors and Affiliations



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.

Corresponding author

Correspondence to Ryohei Yamaguchi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Supplementary information

Supplementary Information

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

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing