Zhang et al. (2025) A multi-model based dataset of global atmospheric moisture source-sink relationships and atmospheric basins

Identification

• Journal: Scientific Data
• Year: 2025
• Date: 2025-11-19
• Authors: Yu Zhang, Rongrong Cai, Mingxi Zhang, Di Xie, Yuan Cao, Yuantao Mei
• DOI: 10.1038/s41597-025-06123-0

Research Groups

• State Key Laboratory of Hydroscience and Engineering, Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
• State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
• International Economic & Technical Cooperation and Exchange Center, Ministry of Water Resources, Beijing, China

Short Summary

This study presents AMSSRAB, a global, multi-model based dataset of atmospheric moisture source-sink relationships (AMSSRs) and derived atmospheric basins (ABs) at 1° resolution over 40 years (1979–2018). It integrates three atmospheric moisture tracking models to provide a robust ensemble representation of the atmospheric hydrological cycle, enabling detailed analysis of regional moisture recycling and climate responses.

Objective

• To develop AMSSRAB, a global 40-year (1979–2018) 1° dataset of atmospheric moisture source-sink relationships (AMSSRs) and derived atmospheric basins (ABs) through network analysis.
• To integrate three atmospheric moisture tracking models (WAM2layers, UTrack, and WaterSip) with different numerical frameworks to reduce individual model uncertainties.
• To apply community detection algorithms to identify ABs as quasi-independent regional moisture circulation systems characterized by high internal recycling.
• To provide seasonal data enabling both climatological analysis and the study of seasonal-scale climate oscillations and long-term trends.

Study Configuration

• Spatial Scale: Global, 1° × 1° grid resolution (65160 map cells, or 57960 for WAM2layers). Comparisons with 0.5° and 1.5° resolution datasets.
• Temporal Scale: 40 years (1979–2018), seasonal (DJF, MAM, JJA, SON). Input data at 3-hourly and 6-hourly intervals.

Methodology and Data

• Models used: WAM2layers (Eulerian), UTrack (Lagrangian), WaterSip (Lagrangian).
• Data sources: European Centre for Medium-Range Weather Forecasts Interim Reanalysis data (ERA-Interim) at 1° × 1° spatial resolution. Variables include surface pressure, model-level horizontal winds, specific humidity, model-level vertical velocity, air temperature, evaporation, and precipitation. Comparisons were made with existing datasets L20, T20, and RECON.

Main Results

• The AMSSRAB dataset provides seasonal atmospheric moisture source-sink relationships at 1° resolution over 40 years (1979–2018) and derived atmospheric basins (ABs).
• The multi-model ensemble approach effectively reduces individual model uncertainties, demonstrating strong agreement with bias-corrected reconstruction data (RECON, R² = 0.84 at grid scale, R² = 0.98 at basin scale) and previously published datasets (L20, T20, R² values 0.67–0.82 at grid scale).
• Derived ABs identify quasi-independent moisture circulation systems with high internal recycling ratios, typically ranging from 50% to 80%.
• ABs exhibit remarkable persistence over the 40-year study period, with boundaries showing both stability and considerable spatial variations.
• The dataset enables tracking of AB evolution and climate responses, such as increasing recycling ratios in the Tibetan Plateau AB during JJA and SON, and significant shifts in the Tropical Pacific Ocean AB during El Niño events.
• Model consistency is high in active moisture flux regions (e.g., tropical and subtropical monsoon areas) but shows substantial discrepancies in extreme climate regions (e.g., polar and arid zones).

Contributions

• Development of the first global, multi-model based 40-year (1979–2018) 1° dataset of atmospheric moisture source-sink relationships (AMSSRs) and derived atmospheric basins (ABs).
• Integration of three atmospheric moisture tracking models (WAM2layers, UTrack, WaterSip) with different numerical frameworks to reduce individual model uncertainties, addressing critical model dependency issues.
• Application of robust community detection algorithms and a refinement procedure to identify and enhance the reliability of ABs as quasi-independent regional moisture circulation systems.
• Provision of a comprehensive regional perspective that complements existing evaporationshed-precipitationshed frameworks.
• Spanning 40 years of seasonal data, enabling both climatological analysis and the study of seasonal-scale climate oscillations and long-term trends in atmospheric moisture circulation.

Funding

• National Natural Science Foundation of China (grant no. 52209026)
• Topology of Hydrosphere Project from the State Key Laboratory of Hydroscience and Engineering (grant no. sklhse-TD-2024-F01)
• Second Tibetan Plateau Scientific Expedition and Research Program (grant no. 2019QZKK0208)

Citation

@article{Zhang2025multimodel,
  author = {Zhang, Yu and Zhong, Deyu and Cai, Rongrong and Zhang, Mingxi and Tian, Yinglin and Xie, Di and Cao, Yuan and Mei, Yuantao},
  title = {A multi-model based dataset of global atmospheric moisture source-sink relationships and atmospheric basins},
  journal = {Scientific Data},
  year = {2025},
  doi = {10.1038/s41597-025-06123-0},
  url = {https://doi.org/10.1038/s41597-025-06123-0}
}