ArticleScientific data2026
A global dataset of land-atmosphere coupling metrics based on observations for evaluating Earth system models.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- A global dataset of land-atmosphere coupling metrics based on observations for evaluating Earth system models.Scientific data · 2026Article
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Authors and funding
2 authors.
Funding
Abstract
A challenge in validating land-atmosphere (L-A) coupling in Earth system models is the limited availability of global observation-based datasets for estimating coupling metrics. This study generates global L-A coupling indices of Pearson correlation coefficient, terrestrial coupling index between soil moisture (SM) and surface heat fluxes, as well as SM memory, at resolutions of 0.1° and 0.25° (with temporal coverage depending on input datasets), employing observationally-based data. The metrics are designed to account for random errors in satellite SM measurements and include quantification of those errors. Multiple SM and flux data products are combined to produce coupling metrics - their spread and error characteristics are used to quantify uncertainty. The global patterns of SM regimes and SM breakpoints of wilting point and critical SM are also quantified through segmented regression analysis. The dataset's performance is validated through inter-product comparisons, documenting high consistency in spatial patterns across different combinations. These datasets are valuable for initialization and validation of weather forecasts, climate modeling, model calibration, and applications in ecology, hydrology, and soil sciences.
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Registered trials
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