The Integrated Multi-proxy Paleoclimate Database for the Northeastern Tibetan Plateau

Li, Yu; Shang, Hao; Yan, Shiyi; Zhang, Zhansen; Gao, MingJun; Xi, Tingting; Zhao, Jiarui; Sun, Wanchen

doi:10.5194/essd-2026-373

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https://doi.org/10.5194/essd-2026-373

Preprints

14 Jul 2026

| 14 Jul 2026

Status: this preprint is currently under review for the journal ESSD.

The Integrated Multi-proxy Paleoclimate Database for the Northeastern Tibetan Plateau

Yu Li, Hao Shang, Shiyi Yan, Zhansen Zhang, MingJun Gao, Tingting Xi, Jiarui Zhao, and Wanchen Sun

Abstract. The Northeastern Tibetan Plateau (NETP) is an important region influenced by the interaction between the Asian monsoon and the Westerlies. Diverse paleoclimate archives, including sedimentary profiles, ice cores, tree rings, surface soils, and meteorological stations, record environmental changes across multiple temporal scales. However, differences in temporal resolution, chronological depth, and proxy sensitivity make direct comparisons among these archives difficult. We present a paleoclimate database for the NETP that integrates these records within a unified framework. The database includes 147 sedimentary profiles, 456 surface soil samples, 23 tree ring width index (RWI) chronologies, 5 ice core records, and data from 73 meteorological stations. The records were standardized using consistent chronological processing and proxy normalization methods to improve cross archive comparability. The database shows internal consistency among regional proxy records. The dataset combines the long term continuity of sedimentary profiles with the high-resolution climate signals preserved in tree rings and ice cores. In addition, integrating surface soil proxies with meteorological observations provides a modern calibration framework for evaluating proxy responses to temperature, precipitation, and elevation gradients. By linking geological archives with instrumental observations across multiple timescales, the dataset supports regional paleoclimate reconstruction and environmental change research in the NETP. The Integrated Multi-proxy Paleoclimate Database for the Northeastern Tibetan Plateau is available at https://doi.org/10.17632/cp7kftnb94.1 (Li, 2026)

Received: 15 May 2026 – Discussion started: 14 Jul 2026

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Yu Li, Hao Shang, Shiyi Yan, Zhansen Zhang, MingJun Gao, Tingting Xi, Jiarui Zhao, and Wanchen Sun

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RC1:
'Comment on essd-2026-373', Anonymous Referee #1, 05 Sep 2026 reply
The manuscript presents a potentially valuable multi-proxy paleoclimate database for the northeastern Tibetan Plateau, integrating sedimentary profiles, surface soils, tree rings, ice cores, and meteorological observations. The regional focus and the diversity of archives are appropriate for an Earth-system data paper, and the integrated framework offers a useful basis for paleoclimate reconstruction and model validation. Overall, the database has clear merits in data synthesis and harmonization. Please find my detailed comments below.

Scientific Comments
The relationship between the defined study area and the spatial extent of the compiled data needs clarification.The study region is explicitly defined as 98.0°–103.5°E, 32.7°–38.2°N (lines 90-93). However, some compiled records, particularly the ice cores and certain meteorological stations, fall well outside this boundary. These records are subsequently used in the analysis (Section 4.2, Fig. 4) to discuss regional climatic features.Please clarify: (1) whether all compiled records were included in the regional analysis, or only a subset falling within the defined boundary; (2) if outside records were included, what rationale was used.

The use of "simple fitted relationships, including linear and low-order polynomial functions" (lines 148-149) is not justified. The criteria for model selection, goodness-of-fit, sedimentation-rate diagnostics, and the propagation of dating uncertainties are not reported. The treatment of different dating methods and materials requires more detail.

Resampling all sedimentary records to 50-year intervals (lines 155-156) and low-resolution ice-core records to an annual scale (lines 191-193) can artificially create or suppress high-frequency variability. Please report the original and effective resolutions for each record, and discuss the potential impact of this homogenization on the results.

The authors state that 45 profiles "with relatively complete records and continuous proxy data" were selected from 147 compiled profiles (lines 159-160). Please specify the quantitative criteria used for this selection. Additionally, briefly describing the general characteristics of the excluded profiles and their differences from the included ones would help readers assess whether the subset is representative of the full database and the study region.

The authors correlate proxy indicators with geographic factors in Fig. 3. However, elevation and coordinates are static spatial variables for each profile, while proxy values are time-series data that vary with depth and age. Correlating the two may be statistically and physically ambiguous. Additionally, adjacent time points within the same profile are autocorrelated, and treating them as independent samples may inflate the effective sample size and increase the risk of false-positive results. In contrast, the spatial regression in Fig. 6 (surface soils vs. modern climate) is methodologically sound. It would be helpful if the authors could: (1) reframe Fig. 3 as exploratory analysis, supplement key statistical information, and clarify its limitations for interpretation; or (2) if the goal is to explore proxy-geography relationships, consider using profile-averaged values or methods that separate spatial and temporal effects.

For the surface soil analysis, the authors mention that MAT and MAP were obtained through “spatial matching” (lines 181-182) but do not specify the data source, reference period, spatial matching method, number of samples with missing values, or the regression model used. Reporting coefficient uncertainties and spatial cross-validation results would also strengthen the analysis.

The authors state that the variance amplitude of pine chronologies is approximately three times that of juniper records (lines 293-295), but do not specify the statistical metric used or whether significance testing was performed. In addition, providing key metadata such as sample depth and expressed population signal (EPS/SSS) for the chronologies would assist readers in assessing chronology quality.

The current tree-ring component of the database consists solely of ring-width index (RWI) chronologies. Tree-ring δ¹⁸O also offers annual resolution but responds more directly to moisture stress and source water changes, and has been widely used in long-term hydroclimatic reconstructions in the arid to semi-arid regions of the Tibetan Plateau. Incorporating δ¹⁸O records into the database would help provide a more comprehensive picture of regional climate signals.Please consider citing the following two studies based on tree-ring δ¹⁸O from the Tibetan Plateau, along with other relevant research:

Yang, B. et al. (2021): Long-term decrease in Asian monsoon rainfall and abrupt climate change events over the past 6,700 years. PNAS, 118(30), e2102007118. DOI: 10.1073/pnas.2102007118
Liu, Y. et al. (2025): Recent centennial drought on the Tibetan Plateau is outstanding within the past 3500 years. Nature Communications, 16, 1311. DOI: 10.1038/s41467-025-56687-z

The authors treat the trend consistency among ice-core δ¹⁸O, tree-ring RWI, and station precipitation during their overlap period as evidence of calibration (lines 357-361). However, direct trend comparison alone cannot distinguish shared climate signals from independent environmental responses, and consistency does not by itself constitute a calibration between archives. In addition, the reported 4-5 year lag (lines 355-357) should be tested using cross-correlation analysis.”

The title emphasizes an “Integrated Multi-proxy Paleoclimate Database,” yet Section 4 largely describes each archive type separately (sedimentary profiles, ice cores, tree rings, surface soils, and stations in separate subsections), with limited cross-archive integration. It would be helpful for the authors to address this issue to better align the title’s emphasis with the content presented.

Technical Corrections
Table 1: The negative values in the “Temporal coverage (CE)” column (e.g., -28050) need clarification (e.g., whether they denote 28050 BCE); the layout should be adjusted.

Line 245: “Large” should not be capitalized.

The naming of “Muztagh” in the text and “Muztagata” in Fig. 7 should be unified.

Please check and correct the formatting and completeness of all references.

The file name proflies_info.xlsx is misspelled and should be corrected to profiles_info.xlsx.

Please check the validity of all data source links provided in the Data Resources column of the database files, as some of these links were found to be inaccessible.

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Citation: https://doi.org/10.5194/essd-2026-373-RC1

Yu Li, Hao Shang, Shiyi Yan, Zhansen Zhang, MingJun Gao, Tingting Xi, Jiarui Zhao, and Wanchen Sun

Data sets

The Integrated Multi-proxy Paleoclimate Database for the Northeastern Tibetan Plateau Li, Y., et al. https://doi.org/10.17632/cp7kftnb94.1

Yu Li, Hao Shang, Shiyi Yan, Zhansen Zhang, MingJun Gao, Tingting Xi, Jiarui Zhao, and Wanchen Sun

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Short summary

Comparing climate records in the Northeastern Tibetan Plateau is challenging due to fragmented archives and varying resolutions. We integrated 147 sedimentary profiles, 456 surface soils, 23 tree ring chronologies, 5 ice core records, and 73 meteorological stations into one database. This shows long-term trends complement precise yearly details in tree rings and ice cores. Matching surface soils with modern weather sets a baseline to accurately map past climates and predict future changes.


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