4.7 Article

Potential controls of isoprene in the surface ocean

期刊

GLOBAL BIOGEOCHEMICAL CYCLES
卷 31, 期 4, 页码 644-662

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1002/2016GB005531

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资金

  1. NERC [NE/K004980/1, NE/K006665/1, NE/I028769/1]
  2. Oceans program
  3. NERC's National Capability program [AMT 23]
  4. NERC
  5. NERC Doctoral Training Grant (DTG)
  6. UK National Centre for Earth Observation
  7. NERC National Capability Atlantic Meridional Transect program
  8. UK Natural Environment Research Council National Capability
  9. National Oceanography Centre, Southampton
  10. Natural Environment Research Council [ncas10008, ncas10009, NE/K004980/1, pml010008, pml010007, pml010009, NE/I028769/1, NE/K006665/1, nceo020006, 1093432, ncas10005] Funding Source: researchfish
  11. NERC [NE/I028769/1, NE/K004980/1, pml010007, pml010009, ncas10005, NE/K006665/1, nceo020006, pml010008, ncas10008, NE/P012930/1] Funding Source: UKRI

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Isoprene surface ocean concentrations and vertical distribution, atmospheric mixing ratios, and calculated sea-to-air fluxes spanning approximately 125 degrees of latitude (80 degrees N-45 degrees S) over the Arctic and Atlantic Oceans are reported. Oceanic isoprene concentrations were associated with a number of concurrently monitored biological variables including chlorophyll a (Chl a), photoprotective pigments, integrated primary production (intPP), and cyanobacterial cell counts, with higher isoprene concentrations relative to all respective variables found at sea surface temperatures greater than 20 degrees C. The correlation between isoprene and the sum of photoprotective carotenoids, which is reported here for the first time, was the most consistent across all cruises. Parameterizations based on linear regression analyses of these relationships perform well for Arctic and Atlantic data, producing a better fit to observations than an existing Chl a-based parameterization. Global extrapolation of isoprene surface water concentrations using satellite-derived Chl a and intPP reproduced general trends in the in situ data and absolute values within a factor of 2 between 60% and 85%, depending on the data set and algorithm used.

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