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Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires

Nature volume 597, pages 370–375 (2021)Cite this article

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Abstract

Droughts and climate-change-driven warming are leading to more frequent and intense wildfires1,2,3, arguably contributing to the severe 2019–2020 Australian wildfires4. The environmental and ecological impacts of the fires include loss of habitats and the emission of substantial amounts of atmospheric aerosols5,6,7. Aerosol emissions from wildfires can lead to the atmospheric transport of macronutrients and bio-essential trace metals such as nitrogen and iron, respectively8,9,10. It has been suggested that the oceanic deposition of wildfire aerosols can relieve nutrient limitations and, consequently, enhance marine productivity11,12, but direct observations are lacking. Here we use satellite and autonomous biogeochemical Argo float data to evaluate the effect of 2019–2020 Australian wildfire aerosol deposition on phytoplankton productivity. We find anomalously widespread phytoplankton blooms from December 2019 to March 2020 in the Southern Ocean downwind of Australia. Aerosol samples originating from the Australian wildfires contained a high iron content and atmospheric trajectories show that these aerosols were likely to be transported to the bloom regions, suggesting that the blooms resulted from the fertilization of the iron-limited waters of the Southern Ocean. Climate models project more frequent and severe wildfires in many regions1,2,3. A greater appreciation of the links between wildfires, pyrogenic aerosols13, nutrient cycling and marine photosynthesis could improve our understanding of the contemporary and glacial–interglacial cycling of atmospheric CO2 and the global climate system.

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Fig. 1: Maps of black carbon AOD and [Chla] anomalies and their historical records.
Fig. 2: Temporal patterns of black carbon AOD and satellite [Chla] in two regions denoted in Fig. 1 during the 2019–2020 Australian wildfire season.
Fig. 3: Plankton blooms observed by in situ measurements from BGC-Argo floats and satellites.
Fig. 4: Enhancement in marine phytoplankton productivity during the 2019–2020 Australian wildfires.

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Data availability

The ESA’s chlorophyll-a products can be accessed at http://www.esa-oceancolour-cci.org/. Satellite aerosol data are available from the Giovanni online data system (https://giovanni.gsfc.nasa.gov/giovanni/). The Copernicus Atmosphere Monitoring Service (CAMS) aerosol reanalysis datasets can be downloaded from the CAMS Atmosphere Data Store (ADS; https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview). The Argo float data are openly available on the Ifremer ftp-server (ftp://ftp.ifremer.fr/ifremer/argo/dac/). The net primary production estimates are available from the Ocean Productivity website (http://sites.science.oregonstate.edu/ocean.productivity/index.php). Access to datasets analysed in this study is also provided in the Methods section. Datasets generated in this study are provided as Source data and at https://doi.org/10.5281/zenodo.4895657. Source data are provided with this paper.

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Acknowledgements

Analyses of satellite aerosol observations used in this study were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC. We thank SeaWiFS and MODIS mission scientists and associated NASA personnel for the production of the data used in this research effort. The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System (https://doi.org/10.17882/42182). W.T. is supported by the Harry H. Hess Postdoctoral Fellowship from Princeton University. N.C. is supported by the “Laboratoire d’Excellence” LabexMER (ANR‐10‐LABX‐19) and co-funded by a grant from the French government under the program “Investissements d’Avenir”. S.B. acknowledges the AXA Research Fund for the support of the long-term research line on Sand and Dust Storms at the Barcelona Supercomputing Center (BSC) and CAMS Global Validation (CAMS-84). P.G.S., J.L., M.M.G.P. and A.R.B. are supported by the Australian Research Council Discovery Projects scheme (DP190103504). P.G.S. and J.W. are supported by the Australian Research Council Centre of Excellence for Climate Extremes (CLEX: CE170100023). J.L. is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754433. A.R.B. is supported by the Australian Research Council Future Fellowship scheme (FT130100037). R.M. is supported by the CSIRO Decadal Climate Forecasting Project. We thank M. Strzelec, M. East, T. Holmes, M. Corkill, S. Meyerink and the Wellington Park Management Trust for help with installation and sampling the Tasmanian aerosol time-series station; A. Townsend for iron aerosol analyses by ICPMS at the University of Tasmania; and A. Benedetti and S. Remy for providing insights on the validation of aerosol reanalysis.

Author information

Author notes

  1. Weiyi Tang

    Present address: Department of Geosciences, Princeton University, Princeton, NJ, USA

  2. These authors contributed equally: Weiyi Tang, Joan Llort

Authors and Affiliations

  1. Division of Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC, USA

    Weiyi Tang, Zuchuan Li & Nicolas Cassar

  2. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia

    Joan Llort, Jakob Weis, Morgane M. G. Perron, Bernadette C. Proemse, Andrew R. Bowie, Christina Schallenberg & Peter G. Strutton

  3. Barcelona Supercomputing Centre, Barcelona, Spain

    Joan Llort & Sara Basart

  4. Australian Research Council Centre of Excellence for Climate Extremes, University of Tasmania, Hobart, Tasmania, Australia

    Jakob Weis & Peter G. Strutton

  5. Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Zuchuan Li

  6. Plymouth Marine Laboratory, Plymouth, UK

    Shubha Sathyendranath & Thomas Jackson

  7. Australian Centre for Research on Separation Science (ACROSS), School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia

    Estrella Sanz Rodriguez

  8. Australian Antarctic Program Partnership, University of Tasmania, Hobart, Tasmania, Australia

    Andrew R. Bowie & Christina Schallenberg

  9. CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

    Richard Matear

  10. CNRS, Univ Brest, IRD, Ifremer, LEMAR, Plouzané, France

    Nicolas Cassar

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  1. Weiyi Tang
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Contributions

This study was conceived by N.C., J.L. and R.M. W.T. and N.C. wrote the manuscript with contribution from co-authors. J.L. and W.T. analysed the spatial distribution and time-series of AOD, aerosol deposition and [Chla], and coordinated the interdisciplinary approach. J.W., C.S. and P.G.S. conducted the analysis of BGC-Argo float observations. S.B. and J.L. conducted the AOD decomposition reanalysis. Z.L. calculated MLD from Argo floats and estimated marine production with W.T. S.S. and T.J. provided and helped with interpretation of satellite observations of [Chla]. M.M.G.P., B.C.P. and A.R.B. collected the aerosol samples and analysed the aerosol Fe content and solubility. E.S.R. analysed levoglucosan in the aerosol samples. All authors contributed to the interpretation of the results.

Corresponding authors

Correspondence to Richard Matear or Nicolas Cassar.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Emission and deposition of aerosols and response of phytoplankton.

a, Monthly aerosol optical depth (AOD) at 550 nm observed by MODIS satellite during the 2019–2020 Australian wildfires from November 2019 to February 2020. b, Monthly chlorophyll-a absolute anomaly. c, Monthly cumulative black carbon aerosol deposition. Contour lines indicate the monthly maximum black carbon AOD (black = 0.05, grey = 0.1, light-grey = 0.15). All: Subtropical, Subantarctic and Polar Fronts are indicated with a dotted, a dashed and a solid black line, respectively48.

Extended Data Fig. 2 Forward trajectories tracking the emission and transport of aerosols from major fire events during the 2019–2020 Australian wildfires.

a, Seven-day trajectories (grey lines) launched every 6 h and originated from wildfires during the period of 26 October to 4 November 2019 and black carbon AOD from the same period shown as the background map. The trajectory origins are depicted by black circles. The distribution of trajectories generally follows the black AOD pattern. b, Seven-day trajectories (grey lines) originated from wildfires during the period of 26 November to 5 December 2019. c, The spatial distribution of 7-day trajectory endpoints frequencies in 2° by 2° grid over the period of November 2019 to January 2020. The trajectory origins are depicted by black circles to represent the major fires’ locations. The 7-day air parcel forward trajectories were launched daily. The red contours depict regions where [Chla] more than doubled during the same period compared with their climatologies. The large [Chla] anomalies generally occurred in regions over which the trajectories passed.

Extended Data Fig. 3 Identification of regions of interest with potential aerosol fertilization.

a, Austral summer (DJF) 2019–2020 averaged chlorophyll-a relative anomaly map with cyan contour lines indicating where the anomaly is equal to 100%. b, Austral summer (DJF) 2019–2020 cumulative deposition of dust and black-carbon with black contour line indicating where deposition is equal to 150 mg m−2. c, Pixels where both [Chla] relative anomaly exceeds 100% and cumulative deposition exceeds 150 mg m−2 are marked in green. Black boxes indicate the South of Australia and Pacific Southern Ocean regions defined in this study. All: Subtropical, Subantarctic and Polar Fronts are indicated with a dotted, a dashed and a solid black line, respectively.

Extended Data Fig. 4 Large chlorophyll-a ([Chla]) anomaly in a big box region of the South Pacific and Southern Ocean during 2019–2020 Australian wildfires.

a, [Chla] anomaly map from December 2019 to February 2020 in comparison to their climatologies. A large portion of the ocean basin (solid black box) was selected to calculate [Chla] time-series. STF, Subtropical Front; SAF, Subantarctic Front. b, Time-series of average [Chla] in the selected box region. Monthly climatological values shown in solid black line. Red and blue areas denote monthly data higher or lower than climatological values, respectively. c, Monthly average [Chla] in individual years in the selected box region. Grey lines, historical years; solid black line, monthly climatologies; dashed black line, 2002 Australian wildfire season; red line, 2019–2020 Australian wildfire season.

Extended Data Fig. 5 Large chlorophyll-a ([Chla]) anomaly in numerous small box regions during 2019–2020 Australian wildfires.

a, [Chla] time-series was calculated in 4,681 of 10° by 10° boxes from 1997 to 2020 in the broad South Pacific and Southern Ocean (20° S–60° S; 120° E–80° W). Yellow circles and yellow dashed boxes are examples to show the center and coverage of each box region. Box moves by 1° eastward and southward sequentially illustrated by the black arrows. Box position 1, 151, 4,531 and 4,681 denoting the edge of the study region are shown as examples on the map of annual [Chla] climatology. The ratio of monthly [Chla] to its monthly climatology is calculated for each 10° by 10° box starting from October 1997 to May 2020. Black circles: centre locations of 10° by 10° boxes where \(\frac{{\rm{m}}{\rm{o}}{\rm{n}}{\rm{t}}{\rm{h}}{\rm{l}}{\rm{y}}[{\rm{C}}{\rm{h}}{\rm{l}}{\rm{a}}]}{[{\rm{C}}{\rm{h}}{\rm{l}}{\rm{a}}]{\rm{c}}{\rm{l}}{\rm{i}}{\rm{m}}{\rm{a}}{\rm{t}}{\rm{o}}{\rm{l}}{\rm{o}}{\rm{g}}{\rm{y}}} > 2.5\) before the 2019–2020 wildfires (from October 1997 to August 2019); red circles: centre locations of 10° by 10° boxes where \(\frac{{\rm{monthly}}[{\rm{Chla}}]}{[{\rm{Chla}}]{\rm{climatology}}} > 2.5\) during or after the 2019–2020 wildfires (from September 2019 to May 2020). Historically, regions with a large anomaly (black circles) are mostly located in coastal waters (for example, east coast of Australia). In contrast, during the 2019–2020 Australian wildfires (red circles), large areas of the open ocean show a high [Chla] anomaly (for example, south of Australia and Pacific sector of the Southern Ocean). Oceanic [Chla] anomalies of this magnitude are unprecedented in the historical record. Some of the black and red circles are on land because a fraction of the 10° by 10° box around these circles covers the ocean. b, Ratio of monthly [Chla] to its corresponding monthly climatologies for each box region from 1997 to 2020. c, Frequency distributions of the monthly [Chla] to monthly climatology ratios over the historical and 2019–2020 austral summers.

Extended Data Fig. 6 Maps of bbp anomalies and comparison between calibrated and uncalibrated BGC Argo in situ bbp measurements.

a, Satellite backscatter bbp relative anomaly for the 2019–2020 austral summer. Bloom region and BGC-Argo float trajectories superimposed on the map. Float positions from September 2019 through March 2020 highlighted. The southern float (red) was in a biologically more active region of the bloom than the two northern floats (blue and yellow). This corroborates the stronger bloom signal shown by the southern float. Dotted, dot-dashed, and solid lines in a represent the climatological positions of the Subtropical Front, Subantarctic Front and Polar Front, respectively48. b, Satellite bbp averaged over two sub-regions encompassing the float paths. The solid lines are 2019–2020 observations and the dotted lines with coloured standard deviation envelopes are the climatology. This analysis corroborates the stronger bloom signal shown by the southern float compared with the two northern floats. c, Comparison between uncalibrated (dashed lines) and calibrated (solid lines) in situ bbp measured by the three BGC-Argo floats. Surface bbp estimates were calculated as the median bbp between 0 and 20 m depth and then calibrated using a linear regression (see Methods for details). The calibration was applied to allow for comparison between float bbp and the satellite-based climatology. The general trend of the float signal is not altered by the calibration.

Extended Data Fig. 7 Iron (Fe) concentration and origin of aerosols collected at an aerosol time-series sampling station in Tasmania during the 2019–2020 Australian wildfires.

a, Total Fe concentration (blue line) during the 2019–2020 Australian wildfire season is compared with the historical median value from 2016–2019 (dashed black line). High levoglucosan concentration (green bar) indicates wildfire-derived aerosols. Grey shaded areas represent samples influenced by anthropogenic sources. See Methods for the use of tracers to track the sources of aerosols. b, Labile Fe concentration (blue line) during the 2019–2020 Australian wildfire season is compared with the historical median value from 2016–2019 (dashed black line). The aerosols with high Fe content collected around 15 January 2020 are likely to have originated from wildfires, indicated by the high concentration of levoglucosan concentrations and low concentration of anthropogenic tracers.

Extended Data Fig. 8 Tracking the origins of aerosols with high iron content collected at an aerosol time-series sampling station in Tasmania during 15–17 January 2020.

a, High black carbon AOD plume passing the sampling station (cyan star). b, Five-day backward trajectories were launched every 6 h from the sampling station (cyan star) during 15–17 January 2020. Both the distribution of trajectories and the frequency of trajectories’ endpoints confirm that the majority of the aerosols came from southeastern Australia where wildfires were raging.

Extended Data Fig. 9 Anomalies in marine phytoplankton productivity during 2019–2020 Australian wildfires.

a, b, Net primary production (NPP) (a) and export production (EP) (b) anomalies in 2019–2020 austral summer relative to their climatologies. Black boxes denote the basin-scale regions (20° S–55° S, 120° E–90° W) used to estimate changes in marine production during the 2019–2020 Australian wildfires.

Extended Data Fig. 10 Relations of large-scale climate patterns to the occurrence of wildfires and to chlorophyll a distribution.

a–c, Time-series of climate indices Indian Ocean Dipole (IOD) (a), Southern Annular Mode (SAM) (b) and Oceanic Niño Index (c). Historical Australian mega-wildfire periods shaded in orange (>1 million hectares of land burned). d, [Chla] anomaly predicted by IOD index during the 2019–2020 Australian wildfires. e, [Chla] anomaly predicted by SAM index during the 2019–2020 Australian wildfires. The [Chla] anomaly potentially induced by the climate patterns are substantially smaller than the observed [Chla] anomaly (Fig. 1d).

Supplementary information

Supplementary Information

This file contains Supplementary Discussion, Tables 1–3, Figs. 1–9, and additional references.

Supplementary Video 1

Animation of daily black carbon aerosol optical depth (BC AOD) derived from CAMS reanalysis during the 2019–2020 Australian wildfires.

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Tang, W., Llort, J., Weis, J. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021). https://doi.org/10.1038/s41586-021-03805-8

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