Spatiotemporal Variability in Modeled Bottom Ice and Sea Surface Dimethylsulfide Concentrations and Fluxes in the Arctic During 1979-2015
GLOBAL BIOGEOCHEMICAL CYCLES(2020)
Abstract
Field observations suggest that oceanic emissions of dimethylsulfide (DMS) may play a dominant role in the production of Arctic aerosols and clouds and therefore modulate the surface irradiance, during spring and summer. DMS is produced not only in the water column but also in various sea ice habitats. The ongoing recession of Arctic sea ice is expected to enhance DMS emissions, but the magnitude of this increase is highly uncertain. Here we investigate the spatiotemporal variability in bottom ice and sea surface DMS concentrations and fluxes using a regional sea ice-ocean physical-biogeochemical model. Model results indicate that the observed accelerated decline of Arctic sea ice extent since the beginning of the 21st century is associated with upward trends in May-August pan-Arctic-averaged sea surface DMS concentration and sea-to-air DMS flux. On the other hand, strong interannual variability and statistically insignificant trends are found for bottom ice DMS concentration and ice-to-sea DMS flux, owing to the counteracting effects of the shrinking horizontal extent and the vertical thinning of sea ice on ice algal production. The pan-Arctic DMS climatology products based on model simulation and satellite algorithms provide dynamically based spatial details that are absent in the in situ measurement-based climatology due to limited spatiotemporal data coverage and inevitable extrapolation bias. Lastly, model results indicate that the bottom ice DMS and its precursor dimethylsulfoniopropionate production can be the only local source of oceanic DMS emissions into the atmosphere during May prior to pelagic blooms, suggesting that it may be a key component of the biological control on Arctic climate at that time.
MoreTranslated text
Key words
sea ice biogeochemistry,Arctic Ocean,dimethylsulfide emission,ice algae,numerical model simulation
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined