Published June 5, 2021
| Version v1
Dataset
Open
Widespread phytoplankton blooms triggered by 2019-2020 Australian wildfires
Authors/Creators
- 1. Princeton University
- 2. Barcelona Supercomputing Centre
- 3. University of Tasmania
- 4. Duke University
- 5. Plymouth Marine Laboratory
- 6. CSIRO Oceans and Atmosphere
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:
- 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.
- 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.
- 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].
- 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]
- 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].
- 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.
- 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.
- 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.
- 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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