Widespread phytoplankton blooms triggered by 2019-2020 Australian wildfires

This dataset is associated with Tang, W., Llort, J., Weis, J. <em>et al.</em> Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. <em>Nature</em> <strong>597, </strong>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: <strong>Aerosol iron data collected during the 2019-2020 Australian wildfires.xlsx</strong>: 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. <strong>MODIS_AOD_mthly_NDJF_2019_2020.nc</strong>: Global monthly mean Aerosol Optical Depth (AOD) at 550nm for the period of November 2019 to February 2020. Source data from NASA-MODIS. <strong>CAMS_AODbc_sum_2019_2020_PAC.nc</strong>:<strong> </strong>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]. <strong>CAMS_AODbc_mthly_NDJF_2019_2020_PAC.nc</strong>:<strong> </strong>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] <strong>CAMS_DustBCdepo_sum_2019_2020.nc</strong>: 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]. <strong>OCCCIv42_chla_DJF_2019_2020_PAC.nc</strong>: 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. <strong>OCCCIv42_chla_mthly_NDJF_2019_2020_PAC.nc</strong>: 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. <strong>Global_net_primary_production.nc</strong>: Average climatological and monthly net primary production rates during the 2019-2020 Australian wildfires estimated by 3 net primary production models. <strong>Global_export_production.nc</strong>: 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., &amp; Leptoukh, G. Online analysis enhances use of NASA earth science data. <em>Eos, Transactions American Geophysical Union</em> <strong>88</strong>, 14-17 (2007). https://giovanni.gsfc.nasa.gov/giovanni/ Behrenfeld, M. J., Boss, E., Siegel, D. A. &amp; Shea, D. M. Carbon-based ocean productivity and phytoplankton physiology from space. <em>Global Biogeochemical Cycles</em> <strong>19</strong>, GB1006 (2005). Behrenfeld, M. J. &amp; Falkowski, P. G. Photosynthetic rates derived from satellite‐based chlorophyll concentration. <em>Limnology and oceanography</em> <strong>42</strong>, 1-20 (1997). Dunne, J. P., Armstrong, R. A., Gnanadesikan, A. &amp; Sarmiento, J. L. Empirical and mechanistic models for the particle export ratio. <em>Global Biogeochemical Cycles</em> <strong>19</strong>, GB4026 (2005). Inness, A., Ades, M., Agusti-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A. M., ... &amp; Suttie, M. The CAMS reanalysis of atmospheric composition. <em>Atmospheric Chemistry and Physics</em> <strong>19</strong>, 3515-3556 (2019). Laws, E. A., D'Sa, E. &amp; 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. <em>Limnology and Oceanography: Methods</em> <strong>9</strong>, 593-601 (2011). Li, Z. &amp; Cassar, N. Satellite estimates of net community production based on O₂/Ar observations and comparison to other estimates. <em>Global Biogeochemical Cycles</em> <strong>30</strong>, 735-752 (2016). Sathyendranath, S.,Jackson, T., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., ... &amp; 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, <em>June 2021</em>. https://catalogue.ceda.ac.uk/uuid/99348189bd33459cbd597a58c30d8d10 Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. &amp; Westberry, T. K. The CAFE model: A net production model for global ocean phytoplankton. <em>Global Biogeochemical Cycles </em><strong>30</strong>, 1756-1777 (2016). Tang, W., Llort, J., Weis, J. <em>et al.</em> Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. <em>Nature</em> <strong>597, </strong>370–375 (2021). Westberry, T., Behrenfeld, M. J., Siegel, D. A. &amp; Boss, E. Carbon-based primary productivity modeling with vertically resolved photoacclimation. <em>Global Biogeochemical Cycles</em> <strong>22</strong>, GB2024 (2008).