A meta‐analysis of oceanic DMS and DMSP cycling processes: Disentangling the summer paradox

Abstract

The biogenic volatile compound dimethylsulfide (DMS) is produced in the ocean mainly from the ubiquitous phytoplankton osmolyte dimethylsulfoniopropionate (DMSP). In the upper mixed layer, DMS concentration and the daily averaged solar irradiance are roughly proportional across latitudes and seasons. This translates into a seasonal mismatch between DMS and phytoplankton biomass at low latitudes, termed the "DMS summer paradox," which remains difficult to reproduce with biogeochemical models. Here we report on a global meta-analysis of DMSP and DMS cycling processes and their relationship to environmental factors. We show that DMS seasonality reflects progressive changes in a short-term dynamic equilibrium, set by the quotient between gross DMS production rates and the sum of biotic and abiotic DMS consumption rate constants. Gross DMS production is the principal driver of DMS seasonality, due to the synergistic increases toward summer in two of its underlying factors: phytoplankton DMSP content (linked to species succession) and short-term community DMSP-to-DMS conversion yields (linked to physiological stress). We also show that particulate DMSP transformations (linked to grazing-induced phytoplankton mortality) generally contribute a larger share of gross DMS production than dissolved-phase DMSP metabolism. The summer paradox is amplified by a decrease in microbial DMS consumption rate constants toward summer. However, this effect is partially compensated by a concomitant increase in abiotic DMS loss rate constants. Besides seasonality, we identify consistent covariation between key sulfur cycling variables and trophic status. These findings should improve the modeling projections of the main natural source of climatically active atmospheric sulfur.

Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales

“…Note also the irregular occurrence of high DMS at the BATS station in late summer in different years ( Fig. 9d; Levine et al, 2016), and that a BATS-like seasonality is not observed at other sites with late summer macronutrient limitation (Archer et al, 2009;Belviso et al, 2012;Galí and Simó, 2015;Vila-Costa et al, 2008). Altogether, these observations suggest that regional macronutrient stress responses are difficult to generalize.…”

“…For example, neural networks were successfully used to estimate DMS in the Arctic (Humphries et al, 2012), but their robustness might be compromised by the small training datasets, the use of climatological variables and the lack of a mechanistic basis. Complex biogeochemical models with satellite data assimilation have strong potential for resolving interannual DMS variations, but reliance on several tens of poorly constrained parameters currently limits their skill (Le Clainche et al, 2010;Galí and Simó, 2015;Tesdal et al, 2016). An approach of intermediate complexity that deserves further exploration is DMS diagnosis based on a simplified steady-state budget equation .…”

Photochemical oxidation of dimethylsulphide to dimethylsulphoxide in estuarine and coastal waters

“…However, a recent meta-analysis of DMS cycling rates suggest that the combined contributions of photochemical and air-sea gas exchange losses generally fall below 20% (Galí and Simo, 2015). Our low water column integrated DMS photo-oxidation rate constants suggest long photochemical turnover times of 86, 53, and 61 days for upper, lower estuarine and coastal waters, respectively.…”

“…Sea surface DMS losses are usually dominated by microbial consumption or photodegradation, with only minor contributions from air-sea exchange (Archer et al, 2002;Toole et al, 2006;Vila-Costa et al, 2008;Galí and Simo, 2015). Results from 35 S-DMS tracer experiments indicate that microbial DMS consumption in surface waters primarily yields dimethylsulphoxide (DMSO) (Del Valle et al, 2007b).…”

Diagnosing sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales

“…For example, neural networks were successfully used to estimate DMS in the Arctic (Humphries et al, 2012), but their predictive power might be compromised by the small training datasets, the use of climatological variables and the lack of a mechanistic basis. Complex biogeochemical models with satellite data assimilation 5 are powerful tools for resolving interannual DMS variations, but reliance on several tens of poorly constrained parameters currently limits their skill (Le Clainche et al, 2010;Galí and Simó, 2015;Tesdal et al, 2015). A pathway of intermediate complexity that deserves further exploration is the remote sensing diagnosis of DMS using a simplified steady-state budget equation, which can account for biotic and abiotic DMS sources and sinks .…”