Published June 5, 2021 | Version v1

Widespread phytoplankton blooms triggered by 2019-2020 Australian wildfires

Description

This dataset is associated with 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. A detailed description of the methods and data analysis can be found in Tang, Llort, Weis et al., 2021. 

 

This dataset contains:

  1. Aerosol iron data collected during the 2019-2020 Australian wildfires.xlsx: Aerosol samples were collected in southern Tasmania, Australia downwind of the 2019-2020 Australian wildfires. Concentrations of total iron, labile iron and levoglucosan in aerosols were measured.
  2. MODIS_AOD_mthly_NDJF_2019_2020.nc: Global monthly mean Aerosol Optical Depth (AOD) at 550nm for the period of November 2019 to February 2020. Source data from NASA-MODIS.
  3. CAMS_AODbc_sum_2019_2020_PAC.nc: Climatological, observed and anomalous black carbon AOD at 550nm accumulated between December 2019 and February 2020 over the Austro-Pacific sector of the Southern Hemisphere, [75ºE-340ºE,10ºS-70ºS]. Based on the daily-averaged outputs of CAMS atmospheric reanalysis. Contains modified Copernicus Atmosphere Monitoring Service Information [2021].
  4. CAMS_AODbc_mthly_NDJF_2019_2020_PAC.nc: Monthly maximum black carbon AOD at 550nm and black-carbon wet deposition between November 2019 and February 2020 over the Austro-Pacific sector of the Southern Hemisphere, [75ºE-340ºE,10ºS-70ºS]. Based on the daily-averaged outputs of CAMS atmospheric reanalysis. Contains modified Copernicus Atmosphere Monitoring Service Information [2021]
  5. CAMS_DustBCdepo_sum_2019_2020.nc: Global dust and black-carbon wet deposition accumulated between December 2019 and February 2020 based on the daily-averaged outputs of CAMS atmospheric reanalysis. Contains modified Copernicus Atmosphere Monitoring Service Information [2021].
  6. OCCCIv42_chla_DJF_2019_2020_PAC.nc: Climatological, observed and anomalous Chlorophyll-a averaged between December 2019 and February 2020 over the Austro-Pacific sector of the Southern Hemisphere, [75ºE-340ºE,10ºS-70ºS]. Based on monthly outputs of Ocean Colour – Climate Change Initiative (ESA, Sathyendranath et al, 2020), version 4.2.
  7. OCCCIv42_chla_mthly_NDJF_2019_2020_PAC.nc: Monthly observed and anomalous Chlorophyll-a between November 2019 and February 2020 over the Austro-Pacific sector of the Southern Hemisphere, [75ºE-340ºE,10ºS-70ºS]. Based on monthly outputs of Ocean Colour – Climate Change Initiative (ESA, Sathyendranath et al, 2020), version 4.2.
  8. Global_net_primary_production.nc: Average climatological and monthly net primary production rates during the 2019-2020 Australian wildfires estimated by 3 net primary production models.
  9. Global_export_production.nc: Average climatological and monthly export production rates during the 2019-2020 Australian wildfires estimated by the combination of 3 models of net primary production and 3 models of export ratio.

 

References:

  • Acker, J. G., & Leptoukh, G. Online analysis enhances use of NASA earth science data. Eos, Transactions American Geophysical Union 88, 14-17 (2007). https://giovanni.gsfc.nasa.gov/giovanni/
  • Behrenfeld, M. J., Boss, E., Siegel, D. A. & Shea, D. M. Carbon-based ocean productivity and phytoplankton physiology from space. Global Biogeochemical Cycles 19, GB1006 (2005).
  • Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite‐based chlorophyll concentration. Limnology and oceanography 42, 1-20 (1997).
  • Dunne, J. P., Armstrong, R. A., Gnanadesikan, A. & Sarmiento, J. L. Empirical and mechanistic models for the particle export ratio. Global Biogeochemical Cycles 19, GB4026 (2005).
  • Inness, A., Ades, M., Agusti-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A. M., ... & Suttie, M. The CAMS reanalysis of atmospheric composition. Atmospheric Chemistry and Physics 19, 3515-3556 (2019).
  • Laws, E. A., D'Sa, E. & Naik, P. Simple equations to estimate ratios of new or export production to total production from satellite‐derived estimates of sea surface temperature and primary production. Limnology and Oceanography: Methods 9, 593-601 (2011).
  • Li, Z. & Cassar, N. Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates. Global Biogeochemical Cycles 30, 735-752 (2016).
  • Sathyendranath, S.,Jackson, T., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., ... & Platt, T. (2020): ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 4.2. Centre for Environmental Data Analysis, June 2021. https://catalogue.ceda.ac.uk/uuid/99348189bd33459cbd597a58c30d8d10
  • Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. & Westberry, T. K. The CAFE model: A net production model for global ocean phytoplankton. Global Biogeochemical Cycles 30, 1756-1777 (2016).
  • Tang, W., Llort, J., Weis, J. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021).
  • Westberry, T., Behrenfeld, M. J., Siegel, D. A. & Boss, E. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Global Biogeochemical Cycles 22, GB2024 (2008).

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