the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Nov 2024
ARGO: ARctic greenhouse Gas Observation metadata version 1
Abstract. Our understanding of how rapid Arctic warming and permafrost thaw affect global climate dynamics is restricted by limited spatio-temporal data coverage due to logistical challenges and the complex landscape of Arctic regions. It is therefore crucial to make best use of the available observations, including the integrated data analysis across disciplines and observational platforms. To alleviate the data compilation process for syntheses, cross-scale analyses, earth system models, and remote sensing applications, we introduce ARGO, a new meta-dataset comprised of greenhouse gas observations from various observational platforms across the Arctic and boreal biomes within the polar region of the northern hemisphere. ARGO provides a centralised repository for metadata on carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) measurements linked with an interactive online tool (https://www.bgc-jena.mpg.de/argo/). This tool offers prompt metadata visualisation for the research community. Here, we present the structure and features of ARGO, underscoring its role as a valuable resource for advancing Arctic climate research and guiding synthesis efforts in the face of rapid environmental change in northern regions. The ARGO meta-dataset is openly available for download at Zenodo (https://doi.org/10.5281/zenodo.13870390) (Vogt et al., 2024).
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Status: open (until 22 Jan 2025)
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RC1: 'Comment on essd-2024-456', Anonymous Referee #1, 16 Dec 2024
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The authors compiled an extensive resource to locating arctic GHG data. It is impressive in scope and thoroughness. This data compilation will be extremely useful. The online tool is friendly and easy to use. Overall, this is a great metadata set.
I have a few comments related to user experience and potentially making the dataset even more user friendly.
I know that it is impossible to identify all data existing, and the authors have done a heroic task identifying many datasets. Nonetheless, you skipped some. I understand that no list can ever be all inclusive, but if not too late, consider adding ESS-DiVE. It is a very good data source with hundreds of arctic datasets. Some of them (but not most) are also available through ORNL-DAAC. For example one of tens of ESS-DiVE datasets you do not link: https://data.ess-dive.lbl.gov/datasets/doi:10.5440/1765733
There’s a bit of a mismatch between the map and the table in the online tool. Ideally, clicking a site (or dataset marker) on the map should then display or highlight the same site (dataset) in the table, and similarly, clicking a line in the table should highlight a site on the map. However, it appears that the map and table are not connected.
A related but different problem – the links to the dataset in the map and the table are not the same. For example – in the table the “link” buttons to the ABOVE_Arctic-CAP datasets (lines 5-10) generate an error (oddly the free text links in the “data availability” columns, do work), while the map provides a working link.
Data frustration – the metadata dataset is a fantastic resource. But I assume that the primary usage of it is to locate data. I tested the experience of a user trying to follow the dataset to locate and obtain data. In many cases it works great, but some cases are frustrating. I categorized these frustrations to types, and bring examples:
Links to large datasets take a large effort to dig out the specific data that is listed in a particular entry in this metadata. I do not know that I have the solution for that. However, in some cases, they do not lead to any actual data (or I wasn’t smart enough to find it). For example: ATQ-207 https://doi.org/10.1038/s41558-017-0066-947 Abbotsford – I followed the link but only found a “site report” and could not figure out how to get the actual data.
Many entries link to a paper. In such cases there is no “link”. I suggest adding the link to the papers DOI or the supplementary dataset that include the actual data as a link button, at the “link” column. In the “data availability” column, you can add the table number within the linked document where the data can be found, to facilitate finding the data more easily.
Some papers do not make the data available. For example, lines 58-59 Abisko (which by-the-way, as far as I could see, appear to be the same and I'm not sure why they are listed as two separate entries). I followed the url to the paper and managed to download it through my university library (another cause of user frustration – datasets not free, at least not to everyone), BUT, there was no data anywhere (tables only show long-term averages, no supplementary material). In cases such as these, if you have personal communications with authors for data that were not made available yet, I suggest publishing the data here and a link to a zenodo dataset. Otherwise, consider not listing data sources that do not actually provide data.
In the maps, you mark some datasets as a point and others as a polygon. As far as I can see, this is done only for airborne datasets. That is a good way to address datasets that offer multiple sites in a particular region. For example, the ABOVE_Arctic-Cap datasets. In the table, these datasets are listed as a particular exact long-lat. Not sure how that particular location was selected, but I suggest enabling a list of long-lats, a link to a polygon or table of points, or a rectangular range (min-max long-lat) in the longitude and latitude columns for datasets that provide data for multiple locations (sub-sites).
In other datasets that provide multiple sites, you break each entry within these datasets to a different entry in your metadata table. For example, entries 22-28 for ATQ (this paper is listed many more times, as ATQ is just one of several sites it provides data for multiple locations in the same tables within the paper). I think it’ll make sense to combine these, similarly to the airborne datasets, such that, at least data of the same type (e.g., chambers) that come from a single source table, are listed once as a single entry for a spatial range.
Citation: https://doi.org/10.5194/essd-2024-456-RC1 -
RC2: 'invited review of essd-2024-456', Anonymous Referee #2, 06 Jan 2025
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The authors present a database and associated online tool gathering and standardizing metadata for many Arctic (and arctic-adjacent) greenhouse gas measurements. The online tool allows for filtering on basic metadata and yields .csv-formatted spreadsheets of metadata for each platform type. Basic metadata are provided for all sites, including data links and PI contacts. More detailed platform-specific metadata are also included. In the manuscript, a short description of the overall framework is presented. Longer descriptions of each platform are included, as well as brief analyses addressing temporal, methodological, and spatial patterns in the measurements. Some gaps in measurement are identified.
Overall I believe this is a useful tool, if properly maintained. It is generally well-described. The overview of ghg monitoring approaches was particularly well-written and helpful; this would be a useful paper to send to new students to acquaint themselves with ghg measurements and initiate a starter project. The writing quality was inconsistent, however, with some very good sections and some poor sections; the bulleted sections need the most revision. I suggest a final writing edit by a single experienced writer to resolve the multiple voices present.
In my opinion, the weakest part of this manuscript is the absence of a clear plan for database maintenance. Without dedicated effort, this database will lose its usefulness fairly quickly. The authors should propose a specific set of tasks for maintenance and a (loose) schedule for accomplishing them. Ideally, recommend responsible personnel, or at least decide on it internally to ensure someone takes responsibility.
Another weakness is the brief and somewhat irregular discussion of data gaps. This is not a metanalysis, but it seems like a missed opportunity to have this very nicely categorized dataset and make so few points about opportunities for improvement. The identification of spatial gaps is useful, but more information about temporal gaps (e.g. most campaigns <1year?) or ecological gaps (e.g. wet tundra more highly studied than alpine tundra?) would fill out the picture. Systematically categorizing these by type of gap – spatiotemporal, platform, or gas species – would aid in organization.
Line items below:
28: Tough sentence to parse. My suggestion with minimal change: “However, the vast size of the Arctic region, in combination with logistical challenges of harsh climate conditions and scarce infrastructure, has permitted the establishment of only sparse observational networks.”
34 – 37: It seems strange to put the example networks and the papers describing them together. Consider mentioning the networks in the flow of the sentence and then placing all journal articles in a citation at the end.
82: flexible -> flexibly;
82 - 83: “allowing to include… biome types” reads strangely. Possible revision: “Allowing for the inclusion of more southerly sites to reduce data gaps for certain biomes and regions”. Consider adding a sentence here referring to a later discussion of what biomes and regions those will be.
84 – 86 “We distinguish…and researchers)” also reads strangely. Possible revision: “We categorize the ecosystem types represented by a study site as barren, cropland, forest, grassland, lake, ocean, reservoir, river, shrubland, tundra, urban, and wetland based on the associated publications or input from site operators and researchers.”
90 – 91 “To categorize study periods…ice-covered season (November–April)” also reads strangely. Please revise
103: ocean-based -> marine
133: Add more citations to cover time period between 1990s and today, e.g. Foken 2011 (book), Baldocchi 2020.
194: and allow to assess -> allow for the assessment of
202: Besides,… -> In addition,…
Figure 3: Explicitly mention that the “sawtooth” appearance of the data is due to seasonal differences in availability. Alternatively, have two traces for each color/data type: one representing all-year data (perhaps solid) and one representing growing season data (perhaps dashed).
266: PI submission =/= accurate! Reword to state almost the opposite: submitted data/metadata are taken as-is from PIs and accuracy is not guaranteed.
268-269: Unclear sentence. Is the idea that PIs will check on their metadata regularly? I think this is unlikely unless there is a formal updating process driven by the ARGO team
Figure 5: There is an inconsistency in the way atmospheric towers and airborne measurements are represented here. Both are previously stated not to have an assigned ecosystem type, but to be assumed terrestrial for “further analysis” (line 103 – 105). It looks like atmospheric towers are assumed to be terrestrial in this figure (all counts in the “terrestrial” category) while airborne platforms are assumed to be neither (all zeroes across both categories). Please resolve or explain.
290: This is a strange comparison because “airborne” and “N2O” are a platform and a GHG species respectively. Additionally, those observations are sparse for different reasons. Open this point differently. Consider also that airborne observations are very information-rich; one chamber measurement offers orders of magnitude less information than one airborne campaign.
297-299: This is interesting and important but narrow. Consider expanding this paragraph to add more context, supported by studies, for these information gaps. For example, resource limitations in certain countries, accessibility concerns, or technological gaps.
Comments on the online tool (for authors’ information; not necessary to address for MS recommendation):
- I appreciate the CSV output (rather than .nc or .xlsx); this will improve accessibility for less code-savvy users (and those of us who keep accidentally mangling timestamps with excel)
- In the online site list, the reference column explodes the row height when many authors are included. Can this be fixed? The Reference_short column seems like it could substitute.
- Consider adding reference and/or data DOI column(s) where applicable.
- The output is helpfully data-rich but I doubt the accuracy of some information. For instance, almost all flux towers will have a radiometer and Temp/RH sensor; this column is NA for many towers. I suppose this is the best you can do with limited info, but I could see users mistakenly throwing away usable data by subsetting on such columns.
- It would be neat to have more sophisticated spatial subsetting, for instance, retrieving measurements within a user-uploaded .kml or .shp.
Citation: https://doi.org/10.5194/essd-2024-456-RC2
Data sets
The ARctic greenhouse Gas Observation meta-database (ARGO) Judith Vogt, Martijn Pallandt, Luana Basso, Abdullah Bolek, Kseniia Ivanova, Mark Schlutow, Gerardo Celis, McKenzie Kuhn, Marguerite Mauritz, Edward Schuur, Kyle Arndt, Anna-Maria Virkkala, Isabel Wargowsky, and Mathias Göckede https://doi.org/10.5281/zenodo.13870390
Model code and software
The ARctic greenhouse Gas Observation meta-database (ARGO) Judith Vogt, Martijn Pallandt, Luana Basso, Abdullah Bolek, Kseniia Ivanova, Mark Schlutow, Gerardo Celis, McKenzie Kuhn, Marguerite Mauritz, Edward Schuur, Kyle Arndt, Anna-Maria Virkkala, Isabel Wargowsky, and Mathias Göckede https://doi.org/10.5281/zenodo.12795381
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