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Finding the Fingerprint of Anthropogenic Climate Change in Marine Phytoplankton Abundance

  • Carbon Cycle and Climate (K Zickfeld, JR Melton and N Lovenduski Section Editor)
  • Published:
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Abstract

Purpose of Review

We review how phytoplankton abundance may be responding to the increase in stratification associated with anthropogenic climate change, providing context on the utility of remote sensing datasets and Earth system model output to understand these perturbations.

Recent Findings

Assessing disruption in the ocean biosphere using remote sensing datasets is challenged by the relatively short length of the observational record, restricting our ability to disentangle fluctuations due to internal climate variability from those imposed by externally forced anthropogenic trends. Ensembles of Earth system models can be used to quantify past and future drivers, but may not skillfully predict observed spatial patterns and temporal dynamics in marine phytoplankton.

Summary

To better understand the role of internal climate variability in the observational record, we construct a synthetic ensemble of global chlorophyll concentration over the MODIS satellite mission using statistical emulation techniques. We emphasize the use of a synthetic ensemble to illuminate the role of internal climate variability in the evolution of the ocean biosphere over time.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Friedlingstein P, Jones MW, O’Sullivan M, et al. Global carbon budget 2019. Earth Syst Sci Data. 2019;11:1783–838.

    Google Scholar 

  2. Falkowski P. The power of plankton. Nature. 2012;483:S17–20.

    CAS  Google Scholar 

  3. McKinley GA, Fay AR, Lovenduski NS, Pilcher DJ. Natural variability and anthropogenic trends in the ocean carbon sink. Annu Rev Mar Sci. 2017;9:125–50.

    Google Scholar 

  4. Bindoff NL, Cheung WWL, Kairo JG, et al. Changing ocean, marine ecosystems, and dependent communities. In: Intergovernmental Panel on Climate Change 2019: Summary for Policymakers. 2019.

  5. Levitus S, Antonov JI, Boyer TP, et al. Global ocean heat content 1955-2008 in light of recently revealed instrumentation problems. Geophys Res Lett. 2009;36:1–5.

    Google Scholar 

  6. Bopp L, Monfray P, Aumont O, et al. Potential impact of climate change on marine export production. Glob Biogeochem Cycles. 2001;15:81–99.

    CAS  Google Scholar 

  7. Lozier MS, Dave AC, Palter B, Gerber LM, Barber RT. On the relationship between stratification and primary productivity in the North Atlantic. Geophys Res Lett. 2011;38:L18609.

  8. Bopp L, Resplandy L, Orr JC, et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences. 2013;10:6225–45.

    Google Scholar 

  9. Krumhardt KM, Lovenduski NS, Long MC, Lindsay K. Avoidable impacts of ocean warming on marine primary production: Insights from the CESM ensembles. Glob Biogeochem Cycles. 2017;31:114–33 This paper uses two ensembles of an Earth system model under different forcing scenarios to identify anthropogenic impacts on marine net primary production.

    CAS  Google Scholar 

  10. Henson SA, Sarmiento JL, Dunne JP, et al. Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity. Biogeosciences. 2010;7:621–40.

    CAS  Google Scholar 

  11. Doney SC, Lima I, Moore JK, et al. Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data. J Mar Syst. 2009;76:95–112.

    Google Scholar 

  12. Deser C, Philips A, Bourdette V, Teng H. Uncertainty in climate change projections: the role of internal variability. Clim Dyn. 2012;38:527–46.

    Google Scholar 

  13. McKinnon KA, Poppick A, Dunn-Sigouin E, Deser C. An “observational large ensemble” to compare observed and modeled temperature trend uncertainty due to internal variability. J Clim. 2017;30:7585–98.

    Google Scholar 

  14. McKinnon KA, Deser C. Internal variability and regional climate trends in an Observational Large Ensemble. J Clim. 2018;31:6783–802 This paper generates a synthetic ensemble of temperature, precipitation, and global sea level pressure.

  15. Sigman DM, Hain MP. The biological productivity of the ocean. Nat Educ. 2012;3:1–16.

    Google Scholar 

  16. Behrenfeld M, O’Malley RT, Boss ES, et al. Revaluating ocean warming impacts on global phytoplankton. Nat Clim Chang. 2016;6:323–30 This paper examines the influence of photoacclimation on phytoplankton intracellular chlorophyll concentrations.

    Google Scholar 

  17. Lalli CM, Parsons TR. Phytoplankton and primary production. In: Biological oceanography: An introduction. Amsterdam: Elsevier Butterworth-Heinemann; 2006.

  18. Giovannoni SJ, Vergin KL. Seasonality in ocean microbial communities. Science. 2012;335:671–6.

    CAS  Google Scholar 

  19. Saba VS, Friedrichs MAM, Antoine D, et al. An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe. Biogeosciences. 2011;8:489–503.

    CAS  Google Scholar 

  20. Sarmiento JL, Gruber N. Organic matter export and remineralization. In: Ocean Biogeochemical Dynamics. Princeton, Woodstock: Princeton University Press; 2006.

    Google Scholar 

  21. Emerson S, Hedges J. Life processes in the ocean. In: Chemical Oceanography and the Marine Carbon Cycle. Cambridge University Press; 2008.

  22. Sunda WG. Trace metal interactions with marine phytoplankton. Biol Oceanogr. 2013;6:411–42.

    Google Scholar 

  23. Moore JK, Doney SC, Glover DM, Fung IY. Iron cycling and nutrient-limitation patterns in surface waters of the World Ocean. Deep-Sea Res II Top Stud Oceanogr. 2002;49:463–507.

    CAS  Google Scholar 

  24. Cheung WWL, Lam VWY, Sarmiento JL, et al. Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Glob Chang Biol. 2010;16:24–35.

    Google Scholar 

  25. Pörtner HO, Karl DM, Boyd PW, et al. Ocean systems. In: Climate Change 2014: Impacts, adaptation, and vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

  26. Rhein M, Rintoul SR, Aoki S, et al. Observations: Ocean. In: Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

  27. Polovina JJ, Howell EA, Abecassis M. Ocean’s least productive waters are expanding. Geophys Res Lett. 2008;35:2–6.

    Google Scholar 

  28. Irwin AJ, Oliver MJ. Are ocean deserts getting larger? Geophys Res Lett. 2009;36:1–5.

    Google Scholar 

  29. Schmittner A, Oschlies A, Matthews HD, Galbraith ED. Future changes in climate, ocean circulation, ecosystems, and biogeochemical cycling simulated for a business-as-usual CO2 emission scenario until year 4000 AD. Glob Biogeochem Cycles. 2008;22:1–21.

    Google Scholar 

  30. Steinacher M, Joos F, Frölicher TL, et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences. 2010;7:979–1005.

    CAS  Google Scholar 

  31. Marinov I, Doney SC, Lima ID, Lindsay K, Moore JK, Mahowald N. North-south asymmetry in the modeled phytoplankton community response to climate change over the 21st century. Glob Biogeochem Cycles. 2013;27:1274–90.

    CAS  Google Scholar 

  32. • Laufkötter C, Vogt M, Gruber N, et al. Drivers and uncertainties in future global marine primary production in marine ecosystem models. Biogeosciences. 2015;12:6955–6984.4 This paper compares projected changes in marine net primary productivity in multiple Earth system under high-emission scenario RCP 8.5.

    Google Scholar 

  33. Kwiatkowski L, Bopp L, Aumont O. Emergent constraints on projections of declining primary production in the tropical oceans. Nat Clim Chang. 2017;7:355–8 This article integrates Earth system model projections and remotely sensed observations to constrain long-term trends in marine primary production.

    CAS  Google Scholar 

  34. Neville RA, Gower JFR. Passive remote sensing of phytoplankton via chlorophyll a fluorescence. J Geophys Res. 1977;82:3487–93.

    Google Scholar 

  35. Meister G, Franz BA, Kwiatkowska EJ, McClain CR. Corrections to the calibration of MODIS Aqua ocean color bands derived from SeaWiFS data. IEEE Trans. 2012;60:310–9.

    Google Scholar 

  36. Siegel DA, Behrenfeld MJ, Maritorena S, et al. Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission. Remote Sens Environ. 2013;135:77–91.

    Google Scholar 

  37. Feng L, Hu C. Cloud adjacency effects on top-of-atmosphere radiance and ocean color data products: a statistical assessment. Remote Sens Environ. 2016;174:301–13.

    Google Scholar 

  38. Gordon HR, Morel AY. Water algorithms. In: Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. New York: Springer-Verlag; 1983.

  39. Maritorena S, Siegel DA, Peterson A. Optimization of a semi-analytical ocean color model for global scale applications. Appl Opt. 2002;41:2705–14.

    Google Scholar 

  40. Hu C, Lee Z, Franz B. Chlorophyll a algorithms for oligotrophic oceans: a novel approach based on three-band reflectance difference. J Geophys Res Ocean. 2012;117:1–25.

    Google Scholar 

  41. Beaulieu C, Henson SA, Sarmiento JL, et al. Factors challenging our ability to detect long-term trends in ocean chlorophyll. Biogeosciences. 2013;10:2711–24.

    Google Scholar 

  42. Henson SA. Slow science: the value of long ocean biogeochemistry records. Philos Trans R Soc. 2014;372:1-22.

  43. Henson SA, Beaulieu C, Lampitt R. Observing climate change trends in ocean biogeochemistry: when and where. Glob Chang Biol. 2016;22:1561–71 This article uses an ensemble of Earth system models to quantify timescales required to detect long-term trends in several biogeochemical variables.

  44. Hammond ML, Beaulieu C, Sahu SK, Henson SA. Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling. Glob Biogeochem Cycles. 2017;31:1103–17.

    CAS  Google Scholar 

  45. Santer BD, Mears C, Doutriaux C, et al. Separating signal and noise in atmospheric temperature changes: the importance of timescales. J Geophys Res Atmos. 2011;116:1–19.

    Google Scholar 

  46. Deser C, Knutti R, Solomon S. Communication on the role of natural variability in future North American climate. Nat Clim Chang. 2012b;2:775–9.

    Google Scholar 

  47. Meehl GA, Hu A. Externally forced and internally generated decadal climate variability associated with the Interdecadal Pacific Oscillation. J Clim. 2013;26:7298–7310.

  48. Schneider DP, Deser C. Tropically driven and externally forced patterns of Antarctic Sea ice change: reconciling observed and modeled trends. Clim Dyn. 2018;50:4099–618.

    Google Scholar 

  49. Behrenfeld MJ, O’Malley RT, Siegel DA, et al. Climate-driven trends in contemporary ocean productivity. Nature. 2006;444:752–5.

    CAS  Google Scholar 

  50. Del Castillo CE, Signorini SR, Karaköylü EM, Rivero-Calle S. Is the Southern Ocean getting greener? Geophys Res Lett. 2019;46:6034–40. This paper highlights regional increases in chlorophyll concentration in the Southern Ocean.

    Google Scholar 

  51. Gregg WW, Rousseaux CS. Global ocean primary production trends in the modern ocean color satellite record (1998–2015). Environ Res Lett. 2019;14:1–9. This paper assimilates ocean color data into an Earth system model to estimate changes in marine primary production.

    Google Scholar 

  52. Boyce DG, Lewis MR, Worm B. Global phytoplankton decline over the past century. Nature. 2010;466:591–6.

    CAS  Google Scholar 

  53. Osman MB, Das SB, Trusel LD, et al. Industrial-era decline in subarctic Atlantic productivity. Nature. 2019;569:551–5. This paper integrates observational datasets in the Arctic to identify regional declines in productivity.

    CAS  Google Scholar 

  54. Saba VS, Friedrichs MAM, Carr ME, et al. Challenges of modeling depth-integrated marine primary productivity over multiple decades: a case study at BATS and HOT. Glob Biogeochem Cycles. 2010;24:1–21.

    Google Scholar 

  55. Gregg WW, Rousseaux CS. Decadal trends in global pelagic chlorophyll: a new assessment integrating multiple satellites, in situ data, and models. J Geophys Res Oceans. 2014;119:5921–33.

    Google Scholar 

  56. Tiao GC, Reinsel GC, Xu D, et al. Effects of autocorrelation and temporal sampling schemes on estimates of trend and spatial correlation. J Geophys Res. 1990;95:507–20.

    Google Scholar 

  57. Weatherhead EC, Reinsel GC, Tiao GC, et al. Factors affecting the detection of trends: statistical considerations and applications to environmental data. J Geophys Res Atmos. 1998;103:17149–61.

    Google Scholar 

  58. Henson S, Cole H, Beaulieu C, Yool A. The impact of global warming on seasonality of ocean primary production. Biogeosciences. 2013;10:4357–69.

    Google Scholar 

  59. Cabré A, Marinov I, Leung S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 Earth system models. Clim Dyn. 2015;45:1253–80.

    Google Scholar 

  60. Leung S, Cabré A, Marinov I. A latitudinally banded phytoplankton response to 21st century climate change in the Southern Ocean across the CMIP5 model suite. Biogeosciences. 2015;12:5715–34.

    Google Scholar 

  61. Marinov I, Doney SC, Lima ID. Response of ocean phytoplankton community structure to climate change over the 21st century: partitioning the effects of nutrients, temperature and light. Biogeosciences. 2010;7:3941–59.

    Google Scholar 

  62. Moore JK, Lindsay SC, Doney MC, Long MC, Misumi K. Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model [CESM1(BGC)]: comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 scenarios. J Clim. 2013;26:9291–312.

    Google Scholar 

  63. Rodgers KB, Lin J, Frölicher TL. Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an earth system model. Biogeosciences. 2015;12:3301–20.

    Google Scholar 

  64. Long MC, Deutsch C, Ito T. Finding forced trends in oceanic oxygen. Glob Biogeochem Cycles. 2016;30:381–97.

    CAS  Google Scholar 

  65. McKinley GA, Pilcher DJ, Fay AR, Lindsay K, Long MC, Lovenduski NS. Timescales for detection of trends in the ocean carbon sink. Nature. 2016;530:469–72.

    Google Scholar 

  66. Lovenduski NS, McKinley GA, Fay AR, Lindsay K, Long MC. Partitioning uncertainty in ocean carbon uptake projections: internal variability, emission scenario, and model structure. Glob Biogeochem Cycles. 2016;30:1276–87.

    CAS  Google Scholar 

  67. Frölicher TL, Rodgers KB, Stock CA, Cheung WWL. Sources and uncertainties in 21st century projections of potential ocean ecosystem stressors. Glob Biogeochem Cycles. 2016;30:1224–43.

    Google Scholar 

  68. Brady RX, Lovenduski NS, Alexander MA, Jacox M, Gruber N. On the role of climate modes in modulating air-sea CO2 fluxes in eastern boundary upwelling systems. Biogeosciences. 2019;16:329–46.

    CAS  Google Scholar 

  69. Schlunegger S, Rodgers KB, Sarmiento JL, et al. Emergence of anthropogenic signals in the ocean carbon cycle. Nat Clim Chang. 2019;9:719–25. This paper identifies the emergence of marine net primary production the latest of several ocean biosphere stressors.

  70. Kay JE, Deser C, Phillips A, et al. The community earth system model (CESM) large ensemble project. Bull Am Meteorol Soc. 2015;96:1333–49.

    Google Scholar 

  71. Gregg WW, Conkright ME. Decadal changes in global ocean chlorophyll. Geophys Res Lett. 2002;29:1-4.

  72. Yoder JA, Kennelly MA. Seasonal and ENSO variability in global ocean phytoplankton chlorophyll derived from 4 years of SeaWiFS measurements. Glob Biogeochem Cycles. 2003;17:1112.

  73. Radenac M, Léger F, Singh A, Delcroix T. Sea surface chlorophyll signature in the tropical Pacific during eastern and central Pacific ENSO events. J Geophys Res. 2012;117:C04007.

  74. Wilks DS. Resampling hypothesis tests for autocorrelated fields. J Clim. 1997;10:65–82.

    Google Scholar 

  75. Theiler J, Eubank S, Longin A, Galdrikian B, Farmer JD. Testing for nonlinearity in time series: the method of surrogate data. Phys D. 1992;58:77–94.

    Google Scholar 

  76. Schreibner T, Schmitz A. Surrogate time series. Phys D. 2000;142:346–82.

    Google Scholar 

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Funding

GWE, NSL, KMK, and RXB are funded by the National Science Foundation (OCE-1752724, OCE-1558225).

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Authors and Affiliations

  1. Department of Geological Sciences and Institute of Arctic and Alpine Research, University of Colorado Boulder, Campus Box 450, Boulder, CO, 80309-0450, USA

    Geneviève W. Elsworth

  2. Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA

    Nicole S. Lovenduski & Riley X. Brady

  3. Department of Statistics and Institute of the Environment and Sustainability, University of California Los Angeles, Los Angeles, CA, USA

    Karen A. McKinnon

  4. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA

    Kristen M. Krumhardt

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  1. Geneviève W. Elsworth
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  2. Nicole S. Lovenduski
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  3. Karen A. McKinnon
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  4. Kristen M. Krumhardt
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  5. Riley X. Brady
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Correspondence to Geneviève W. Elsworth.

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Elsworth, G.W., Lovenduski, N.S., McKinnon, K.A. et al. Finding the Fingerprint of Anthropogenic Climate Change in Marine Phytoplankton Abundance. Curr Clim Change Rep 6, 37–46 (2020). https://doi.org/10.1007/s40641-020-00156-w

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