the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
MAP-IO, an atmospheric and marine observatory program onboard Marion Dufresne over the Southern Ocean
Abstract. This article is devoted to the presentation of the MAP-IO observation program. This program, launched in early 2021, has enabled the observation of nearly 700 days of measurements over the Indian and Southern Ocean thanks to the equipment of 17 meteorological and oceanographic scientific instruments on board the ship Marion Dufresne. Several observation techniques have been developed to respond to the difficulties of observations on board ships, in particular for passive remote sensing data, as well as quasi-autonomous data acquisition and transfer. The first measurements made it possible to draw up unprecedented climatological data of the Southern Ocean of the size distribution and optical thickness of aerosols, of the concentration of trace gases and greenhouse gases, of UV, and of integrated water vapor. High resolution observations of phytoplankton in surface waters have also shown a great variability in latitude, in terms of abundance and community structure (diversity). The operational success of this program and these unique scientific results all together establish a proof of concept and underline the need to transform this program into a permanent observatory.
- Preprint
(7124 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 16 May 2024)
-
RC1: 'Comment on essd-2023-531', Anonymous Referee #1, 14 Apr 2024
reply
Tulet et al present an observational dataset from a shipborne campaign (MAP-IO) over the Southern Ocean from Jan 2021-June 2023. I want to congratulate the team for overcoming many challenges to make the campaign possible. This is a valuable dataset because it contains many important parameters (such as climatology, gases, aerosols, and phytoplankton) over the Southern Ocean where lacks of observations. The authors present some interesting results and potential research that can be explored with the data. However, I would expect more information on data quality and data processing because this is a data description paper, and that is why I recommend a major revision. Besides that, I think the authors should improve the language and text clarity, for which I have listed some suggestions below.
Major comments:
- Data quality: The authors should provide more information (particularly for the instruments in section 3.3) on instrument calibration, precision, measurement frequency, and how temperature, humidity and air pressure impact the measurement. Some information can be provided in the supplementary materials.
- Data processing: The authors mentioned in several places that some data are filtered out manually by the PIs of the instruments. This doesn’t sound like a good practice. The authors should provide more details on how PIs manually excluded data (criteria for each instrument). Similarly, there is an automatic flags calculation system (in section 4.2.3) without any details about how it is done.
Minor comments:
- L33-38: This part is a bit disconnected from the previous and later texts. I’d recommend first adding 1-2 sentences to explain why the Southern Ocean is important, to connect with the previous texts (that observations over oceans are limited). Then after the 3 pathways of BDC, maybe add some texts to explain why these pathways are related/important to Southern Ocean. Now the transition from this part to L38 “Although two stations” is confusing to me.
- L41-43: this also needs some transitions between the two sentences. The first sentence “The observation of …” talks about barriers, then suddenly the next sentence talks about tropospheric transport.
- L197: do the 4 cylinders have different concentrations? What are the concentration ranges of calibration standards for these gases as well as for other gases and aerosols?
- L222: should add numbers or a plot (in the supplement) to show the uncertainty.
- Section 3.4.1: what is the precision and detection limit? How are the cloudy days processed?
- L436-437: in the publicly available dataset, are data filtered by both NO and CO methods?
- Figure 11: why so many CN data are removed for the regions around [35S,75E] to [40S,80E] compared to other gases (e.g. Figure 9 same regions)?
- Section 7: I randomly checked the CO, CO2 data, but there is no latitude, or longitude info in the dataset, why?
Specific comments:
Keywords: missing
L2: consider using ‘with’ instead of ‘thanks to’
L3: observation – observational
L4: ships – the ship [if there was only one ship]
L10: the journal usually requires a sentence to include the data source/link, please check with the journal.
Abstract: I recommend adding 1-2 sentences at the end to mention the potential implications of this dataset or science questions that can be explored with the data.
L12: remove ‘probably’
L17: what is ‘earth climate budget’?
L18: estimates – estimated
L25: as well – such as
L27: add what WMO stands for
L61: have to – should
L90: A – The
L91: a third one – the third one
L100: The rest of – During the rest of
L104: is to carry – carries; integrated – integrates
L105: remove ‘a focus of’; interest for - interest in
L106: to establish – of establishing
L120: at – in; move ‘by July 2023’ to the end of the sentence
L129: pollution - contamination?
L130: remove ‘located’
L211: add ‘which is’ before ‘able to’
L330: it – is
L448: 90% of the 2021-2023 measurement days or of all the days during 2021-2023 (365 days/year)?
L470: what evidence supports the statement ‘This corresponds to…’?
L475: what range of quantiles, 25-75%?
L476: correctly – normally
Figure 9: should explain in the figure legend what the texts above the dates (such as ‘OP1-2021’) represent.
Figure 10: (1) x-axis represents month? (2) what are the ranges of quantiles?
Figure 13: missing x-axis label
Citation: https://doi.org/10.5194/essd-2023-531-RC1
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
288 | 70 | 14 | 372 | 17 | 20 |
- HTML: 288
- PDF: 70
- XML: 14
- Total: 372
- BibTeX: 17
- EndNote: 20
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1