Patterns and drivers of dimethylsulfide concentration in the northeast subarctic Pacific across multiple spatial and temporal scales

• Published in: Biogeosciences
• Main Authors: Herr, Alysia E., Kiene, Ronald P., Dacey, John W. H., Tortell, Philippe D.
• Format: Text
• Language: English
• Published: 2019
• Subjects: Pacific; Subarctic
• Online Access: https://doi.org/10.5194/bg-16-1729-2019; https://www.biogeosciences.net/16/1729/2019/

The northeast subarctic Pacific (NESAP) is a globally important source of the climate-active gas dimethylsulfide (DMS), yet the processes driving DMS variability across this region are poorly understood. Here we examine the spatial distribution of DMS at various spatial scales in contrasting oceanographic regimes of the NESAP. We present new high-spatial-resolution measurements of DMS across hydrographic frontal zones along the British Columbia continental shelf, together with key environmental variables and biological rate measurements. We combine these new data with existing observations to produce a revised summertime DMS climatology for the NESAP, yielding a broader context for our sub-mesoscale process studies. Our results demonstrate sharp DMS concentration gradients across hydrographic frontal zones and suggest the presence of two distinct DMS cycling regimes in the NESAP, corresponding to microphytoplankton-dominated waters along the continental shelf and nanoplankton-dominated waters in the cross-shelf transitional zone. DMS concentrations across the continental shelf transition (range < 1–10 nM, mean 3.9 nM) exhibited positive correlations to salinity ( r =0.80 ), sea surface height anomaly (SSHA; r =0.51 ), and the relative abundance of prymnesiophyte and dinoflagellates ( r =0.89 ). In contrast, DMS concentrations in nearshore coastal transects (range < 1–24 nM, mean 6.1 nM) showed a negative correlation with salinity ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>r</mi><mo>=</mo><mo>-</mo><mn mathvariant="normal">0.69</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="49pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="1d720f16ffc1c28e18cb5a2217ddede9"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-16-1729-2019-ie00001.svg" width="49pt" height="10pt" src="bg-16-1729-2019-ie00001.png"/></svg:svg> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>r</mi><mo>=</mo><mo>-</mo><mn mathvariant="normal">0.78</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="49pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="7dc62c8dbec92e239cbf2fc4140accec"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-16-1729-2019-ie00002.svg" width="49pt" height="10pt" src="bg-16-1729-2019-ie00002.png"/></svg:svg> ) and SSHA ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>r</mi><mo>=</mo><mo>-</mo><mn mathvariant="normal">0.81</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="49pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="916caef5c7b14c24d14f24fc96676cb7"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-16-1729-2019-ie00003.svg" width="49pt" height="10pt" src="bg-16-1729-2019-ie00003.png"/></svg:svg> <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>r</mi><mo>=</mo><mo>-</mo><mn mathvariant="normal">0.75</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="49pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="f732df8757d4b8f4b66a6c1e71131673"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-16-1729-2019-ie00004.svg" width="49pt" height="10pt" src="bg-16-1729-2019-ie00004.png"/></svg:svg> ) and a positive correlation to relative diatom abundance ( r =0.88 r =0.86 ). These results highlight the importance of bloom-driven DMS production in continental shelf waters of this region and the role of prymnesiophytes and dinoflagellates in DMS cycling further offshore. In all areas, the rate of DMS consumption appeared to be an important control on observed concentration gradients, with higher DMS consumption rate constants associated with lower DMS concentrations. We compiled a data set of all available summertime DMS observations for the NESAP (including previously unpublished results) to examine the performance of several existing algorithms for predicting regional DMS concentrations. None of these existing algorithms was able to accurately reproduce observed DMS distributions across the NESAP, although performance was improved by the use of regionally tuned coefficients. Based on our compiled observations, we derived an average summertime distribution map for DMS concentrations and sea–air fluxes across the NESAP, estimating a mean regional flux of 0.30 Tg of DMS-derived sulfur to the atmosphere during the summer season.