Tamara L. Schlosser, Peter G. Strutton, Kirralee Baker, Philip W. Boyd
{"title":"Latitudinal Trends in Drivers of the Southern Ocean Spring Bloom Onset","authors":"Tamara L. Schlosser, Peter G. Strutton, Kirralee Baker, Philip W. Boyd","doi":"10.1029/2024JC021099","DOIUrl":null,"url":null,"abstract":"<p>The Southern Ocean spring phytoplankton bloom impacts regional food webs and the marine carbon cycle, but we do not fully understand which drivers—environmental, ecological, or biological—control the timing of the onset of the spring bloom. Nutrients, particularly iron, are likely replete in the austral winter, but the importance of underwater light availability and grazing pressure are topics of ongoing discussion. Furthermore, in the extreme polar winter, phytoplankton physiology may impart additional constraints on the bloom onset. We analyzed biogeochemical (BGC) Argo profiles from the Pacific sector of the Southern Ocean, and a one-dimensional water column turbulence model forced by reanalysis data. Though the surface mixed layer defines where density is homogenous, the presence of enhanced turbulence and the active mixing of constituents, such as chlorophyll fluorescence, is better estimated by the depth of active mixing that we estimate from the turbulence model. We identified two regimes: one north of the subantarctic front where bloom onsets occur around July, before the seasonal maximum in mixing depth and when light availability remained near its annual minimum value. It is likely that changes in the phytoplankton loss rate control the bloom onset in this region. South of the subantarctic front, bloom onsets occur closer to austral spring following enhanced light availability, suggesting that bloom onset is primarily controlled by phytoplankton growth rather than loss terms. Our analysis shows that new insights can be gained into spring bloom phenology from the combination of float and model data.</p>","PeriodicalId":54340,"journal":{"name":"Journal of Geophysical Research-Oceans","volume":"130 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research-Oceans","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JC021099","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OCEANOGRAPHY","Score":null,"Total":0}
引用次数: 0
Abstract
The Southern Ocean spring phytoplankton bloom impacts regional food webs and the marine carbon cycle, but we do not fully understand which drivers—environmental, ecological, or biological—control the timing of the onset of the spring bloom. Nutrients, particularly iron, are likely replete in the austral winter, but the importance of underwater light availability and grazing pressure are topics of ongoing discussion. Furthermore, in the extreme polar winter, phytoplankton physiology may impart additional constraints on the bloom onset. We analyzed biogeochemical (BGC) Argo profiles from the Pacific sector of the Southern Ocean, and a one-dimensional water column turbulence model forced by reanalysis data. Though the surface mixed layer defines where density is homogenous, the presence of enhanced turbulence and the active mixing of constituents, such as chlorophyll fluorescence, is better estimated by the depth of active mixing that we estimate from the turbulence model. We identified two regimes: one north of the subantarctic front where bloom onsets occur around July, before the seasonal maximum in mixing depth and when light availability remained near its annual minimum value. It is likely that changes in the phytoplankton loss rate control the bloom onset in this region. South of the subantarctic front, bloom onsets occur closer to austral spring following enhanced light availability, suggesting that bloom onset is primarily controlled by phytoplankton growth rather than loss terms. Our analysis shows that new insights can be gained into spring bloom phenology from the combination of float and model data.