In:
Earth System Science Data, Copernicus GmbH, Vol. 14, No. 7 ( 2022-07-05), p. 2963-2987
Abstract:
Abstract. This paper presents an updated estimation of the bottom-up global surface
seawater dimethyl sulfide (DMS) climatology. This update, called DMS-Rev3,
is the third of its kind and includes five significant changes from the last
climatology, L11 (Lana
et al., 2011), that was released about a decade ago. The first change is the
inclusion of new observations that have become available over the last
decade, creating a database of 873 539 observations leading to an
∼ 18-fold increase in raw data as compared to the last
estimation. The second is significant improvements in data handling,
processing, and filtering, to avoid biases due to different observation
frequencies which result from different measurement techniques. Thirdly, we
incorporate the dynamic seasonal changes observed in the geographic
boundaries of the ocean biogeochemical provinces. The fourth change involves
the refinement of the interpolation algorithm used to fill in the missing
data. Lastly, an upgraded smoothing algorithm based on observed DMS
variability length scales (VLS) helps to reproduce a more realistic
distribution of the DMS concentration data. The results show that DMS-Rev3
estimates the global annual mean DMS concentration to be ∼ 2.26 nM (2.39 nM without a sea-ice mask), i.e., about 4 % lower than the
previous bottom-up L11 climatology. However, significant regional
differences of more than 100 % as compared to L11 are observed. The global
sea-to-air flux of DMS is estimated at ∼ 27.1 TgS yr−1,
which is about 4 % lower than L11, although, like the DMS distribution,
large regional differences were observed. The largest changes are observed
in high concentration regions such as the polar oceans, although oceanic
regions that were under-sampled in the past also show large differences
between revisions of the climatology. Finally, DMS-Rev3 reduces the
previously observed patchiness in high productivity regions. The new climatology, along
with the algorithm, can be found in the online repository:
https://doi.org/10.17632/hyn62spny2.1 (Mahajan,
2021).
Type of Medium:
Online Resource
ISSN:
1866-3516
DOI:
10.5194/essd-14-2963-2022
DOI:
10.5194/essd-14-2963-2022-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2022
detail.hit.zdb_id:
2475469-9
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