the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jan 2024
Spatially Extensive Long-term Quality-assured Land-atmosphere Interactions Dataset over the Tibetan Plateau
Abstract. Climate over the Tibetan Plateau (TP) has undergone substantial changes in recent decades due to its sensitive to global climate change. Regional land-atmosphere interactions are closely linked to the changes that have emerged across the TP. The TP is recognized as an ideal natural laboratory for monitoring ongoing climate changes and examining the mechanisms of land-atmosphere interactions over this high mountain region with diverse landscapes. Current models and satellites are struggle to accurately depict the interactions, critical field observations on land-atmosphere interactions here therefore provide necessitate independent validation data and fine-scale process insights for constraining reanalysis products, remote sensing retrievals, and land surface model parameterizations. Scientific data sharing is crucial for the TP since acquiring critical field observations under this diverse topography and harsh conditions is challenging. However, in-situ observations are currently scattered among individuals or small groups and have yet to be integrated for comprehensive analysis, preventing a better understanding of the interactions, the unprecedented changes they generate, and the substantial massive ecological and environmental consequences they bring about. Here, we collaborated with different agencies and organizations to present a comprehensive dataset for hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions spanning up to 17 years (2005–2021). This dataset is compiled from 12 field stations covering typical TP landscapes, and represents the most extensive in-situ observation data available for studying land-atmosphere interactions on the TP to date in terms of both spatial coverage and duration. To assure data quality, a set of rigorous data processing and quality control procedures are implemented for all observation elements in this dataset. The operational workflow and procedures are individually tailored to the varied types of elements at each station, including automated error screening, manual inspection, diagnostic checking, adjustments, and quality flagging. The hourly data series, the quality-assured data, and supplementary information including data integrity and the percentage of correct data on a monthly scale are provided via the National Tibetan Plateau Data Center (https://doi.org/10.11888/Atmos.tpdc.300977, Ma et al., 2023). The current dataset provides the benchmark constraints needed to both evaluate and improve the land surface models, reanalysis products, and remote sensing retrievals. Additionally, it is capable of characterizing fine-scale processes for land-atmosphere interactions of the TP and the underlying influence mechanisms.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2024-9', Anonymous Referee #1, 21 Feb 2024
The manuscript presents a comprehensive dataset detailing land-atmosphere interactions over the Tibetan Plateau, derived from 12 field stations covering a range of landscapes. This dataset encompasses hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions for up to 17 years (2005-2021). However, I have several major concerns that the authors should address.
1) Section 2 provides extensive detail on the observation infrastructure and data post-processing workflow, including data processing, quality control, gap filling, and archiving procedures. The authors should include more explicit information on the calibration of instruments across different stations and the rationale behind the selection of specific quality control algorithms. Comparisons with standard practices in the field could help in benchmarking the dataset's reliability.
2) The authors should provide a comprehensive and detailed explanation of the data collection methods and quality control procedures employed in their study. Instead of merely listing various methodologies, it is crucial to elaborate on how data was gathered, the criteria used for data selection, and the specific steps taken to ensure the integrity and accuracy of the data.
3) While the approach for handling missing data through linear temporal interpolation is mentioned in 2.3.3 Gap filling, a discussion on the impact of these interpolations on the dataset's overall quality and potential biases introduced should be mentioned. Including statistical metrics to quantify the robustness of the gap-filled data could enhance the dataset's credibility.
4) Section 3 on different datasets are well-detailed but the authors should add specific examples of data validation against external measurements or models, if available. This could include inter-comparison with satellite data, other observational networks, or model outputs to validate the spatial and temporal accuracy of the dataset.
- AC1: 'Reply on RC1', Zhipeng Xie, 12 Apr 2024
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RC2: 'Comment on essd-2024-9', Shiqin Xu, 01 Mar 2024
The paper by Ma et al. focuses on generating in situ records relating to land-atmosphere interactions through an integrated observations network across the Tibetan Plateau. This work is immensely important for understanding the behavior of atmospheric boundary layer across various landscapes over the Tibetan Plateau, where site observations are notably scarce. Moreover, those measurements can be used for calibrating and assessing land surface models and remote sensing observations. The following comments warrant attention.
1. Abstract needs to be concise. The first two sentences had provided background information, please delete the sentence ‘The TP is recognized … with diverse landscape’. Remove the content ‘Scientific data sharing is critical for the TP … they bring about’ into main text. Include more information about which kind of variable you are going to provide and temporal extent.
2. Section 2.1 and 2.2: Please provide a table in which each row represents one site and each column include one unique information. Then please provide the site name, location, climate, landscape type, installation of infrastructure, and measuring variables. If it is too large. It would be OK to provide two tables. One for basic information and another for introducing infrastructure installation, managing period, and measuring variables. Please provide as much details as you can for publishing a data paper.
3. Section ‘2.3 Data post-processing workflow’ needs further improvements.
(1) Figure 2: The information provide in this figure is a little bit general. It should be a summary of section 2.3.1 to 2.3.4. (i) We need to know the specific variables you are working on. (ii) Are you using those data processing approach for all variables? (iii) In the four modules, are you consistently applied these processing approaches to each variable and each site? I highly recommend that the author refer to previously published ESSD or other high-quality data papers and redesign the flowchart accordingly. I have provided the following paper for reference. Please note that there is no need to cite them.
Gebrechorkos, S. H., Peng, J., Dyer, E., Miralles, D. G., Vicente-Serrano, S. M., Funk, C., . . . Dadson, S. J. (2023). Global high-resolution drought indices for 1981–2022. Earth Syst. Sci. Data, 15(12), 5449-5466.
Pastorello, G., Trotta, C., Canfora, E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020).
Beck, H. E., E. F. Wood, M. Pan, C. K. Fisher, D. G. Miralles, A. I. J. M. van Dijk, T. R. McVicar, and R. F. Adler, 2019: MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment. Bull. Amer. Meteor. Soc., 100, 473–500.
(2) Section 2.3.1 to 2.3.4 require much more details: (i) Please list the relevant methods (equation, models, quantification metrics, etc) you used where are applicable. (ii) Definition of missing data should be quantified for each variable and each site. (iii) Provide a detailed description of the data header file format. Overall, this part is very important and much more details should be provided.
4. Section 3 Data description: Much more details should be provided. Provide a table and listed all those variables this data set will provide. Indicate availability of each variable at a specific site. Provide unit for each variable and start date and end date (if applicable). The primary principle is assisting the data user quickly know how those valuable measurements fit their research.
5. Section 4: it would be great if the authors can provide some application cases.
Citation: https://doi.org/10.5194/essd-2024-9-RC2 - AC2: 'Reply on RC2', Zhipeng Xie, 12 Apr 2024
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RC3: 'Comment on essd-2024-9', Anonymous Referee #3, 13 Mar 2024
This manuscript provides an overview of in-situ observations of land-atmosphere interactions at 12 unique sites across the Tibetan Plateau (TP). The authors first identify and describe the standard flux tower (e.g., EC, meteorology, soil) measurements collected at each site (types of instruments and heights) and then outline the quality control and quality assurance processes that are completed, before examining the seasonal and diurnal trends between each site. The work is important and novel. I have a few general comments:
1.) Introduction - The introduction follows a logical framework: importance of TP with regards to Earth system interactions, how the the TP is warming faster than other areas (and the implications), importance of models and datasets for decision making, challenges with model data inputs due to scarcity of in-situ observations, past efforts, and then potential issues (QA/QC of data) with open access datasets, but in it's current state it is a bit long (mainly the first, third, and fifth paragraphs). I would recommend trimming the introduction if possible.
2.) Observation Network and Data Processing - Similar to some of the other referee comments, I would like to see more specific details outlining the typical on-site calibrations and maintenance of instruments at each site and better address how you compare measurements at varying heights between sites (e.g., from Table 1 - EC heights ranging from 3 to 4.5 m, and met observations from 1.5 m, 2.75m, or 5 and 10 m).
3.) Eddy Covariance Data - Were there any differences found between the LI-7500s and the EC150 at Maqu? Was this examined? You might cite a supporting paper to address this if applicable. Also, skipping a bit ahead, but in Figure B3, all of the sensible heat (H) data are marked as 'bad' data quality. Why is this? Why are these data still considered/highlighted in the manuscript if they are so bad (Figure 8) ? Similarly, how can there be very good LE data but bad H data if they are both being derived from the H2O flux in the EC setup? Please address.
4.) Data Descriptions - I have some general questions/comments about Section 3. Could the higher nighttime wind speeds at Yakou be attributed to the higher measurement height (10 m at that site vs 1 m at other sites)? What benefit do the pressure data provide given the different site altitudes? Can you comment on the diurnal offset in H and LE at Jingyangling (Figure 8)? All others sites in Figure 8 follow a similar trend, except for Jingyangling, does this mean H and LE are peaking at night? Lastly, since this is a data paper, it might be better to forgo the results and site comparisons outlined in much of Section 3, and instead provide a brief comparison of how these in-situ data stack up against aforementioned model or remote sensing data within the TP.
Citation: https://doi.org/10.5194/essd-2024-9-RC3 - AC3: 'Reply on RC3', Zhipeng Xie, 12 Apr 2024
Data sets
Spatially Extensive Long-term Quality-assured Land-atmosphere Interactions Dataset over the Tibetan Plateau Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li https://doi.org/10.11888/Atmos.tpdc.300977
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