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 872,427 observations leading to a ~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 results 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. And finally, 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 ~1.87 nM (2.35 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 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.
Third Revision of the Global Surface Seawater Dimethyl Sulfide Climatology (DMSRev3)
by Shrivardhan Hulswar et al.
The paper is well written and easy to follow. The description of mechanisms producing DMS is very clear also for non expert people. The new climatology includes new data and this is a paper added value.
Weak points are related to statistics, number of useful data for climatology, spatial and temporal distribution. These problems that are presented by the authors but not resolved.
The initial data set consisted of 872,427 data points of which only 48,567 are used after post processing. Therefore, the spatial and temporal coverage is worse than that shown in figure 1. Hence a first series of questions: in each month how many data are available in all geographical areas of 1 ° x 1 °? Does each square of 1x1 have a statistically significant number of data points? Are the data for each month and 1x1 areas statistically sufficient or should authors examine them seasonally?
The climatology obtained in data-poor areas with similarity-estimated VLS is not convincing, by taking into account that the phenomena under investigation are occurring at high frequency and varying from place to place.
Amother point that the authors present but do not investigate is related to methodologies and technologies for data collection. Over the years they have changed and so has the data accuracy. In the paper there should be an indication of what is the final accuracy of the climatology in the various areas.
The authors should also provide information on calibration standard if exists and if used in data selection.
The problem of ecological provinces is well posed, and the authors refer to previously published articles. The 'geographically homogeneous' data can be identified with a cluster analysis. In a non-static environment it is possible that geographic homogeneity may vary over time. Authors should discuss this.
The new data included in the paper makes it interesting and publishable after major revision.
Special comments.
Figure 1 should shows the total raw data (1a) and those used for climatology (1b).
An indication of errors or accuracy in the various regions would be desirable
The third climatological estimation of sea-surface DMS concentrations based on in-situ measurements was created. The update includes a much larger input dataset and includes improvements in the data unification, filtering, and smoothing algorithm. The DMS-Rev3 climatology provides more realistic monthly estimates of DMS and shows significant regional differences compared to the past climatologies.
The third climatological estimation of sea-surface DMS concentrations based on in-situ...
