A New Benchmark for Land-Atmosphere Coupling: Correcting Observational Metrics for Validating CESM-Derived Estimates

2025 · doi:10.22541/au.174466002.25009692/v1
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This preprint studies land–atmosphere coupling by constructing observationally derived soil-moisture (SM) coupling metrics that account for stochastic errors in satellite SM, then applying them to evaluate land–atmosphere estimates from the Community Earth System Model (CESM). Using global gridded SM time series from SMAP L3, ESA CCI v08.1, and machine-learning SM (SoMo.ml), along with surface heat fluxes/evapotranspiration from GLEAM, FluxCom, and CAMELE, the authors correct SM variability assumptions (treated as a first-order Markov process) and compute corrected coupling measures such as Pearson correlations between SM and surface fluxes. They also analyze regime distributions using corrected SM “memory” and breakpoints like wilting point and critical soil moisture, and they validate the corrected metrics against AmeriFlux in-situ measurements. A major limitation noted is that the work is preliminary and not peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Soil moisture (SM) plays a crucial role in land-atmosphere (LA) interactions by regulating evapotranspiration and influencing moisture and energy budgets. However, challenges persist in quantifying LA coupling due to inconsistencies across observational datasets, reanalysis products, and model simulations. This study addresses these challenges by developing observationally derived LA coupling metrics while accounting for stochastic errors in satellite-based soil moisture measurements. These metrics are then applied to the Community Earth System Model (CESM) to provide a robust framework for model validation.  Global observationally gridded LA coupling metrics are constructed using SM time series from the Soil Moisture Active Passive (SMAP L3) satellite, the Climate Change Initiative (ESA CCI v08.1), and machine learning-derived soil moisture (SoMo.ml), alongside observation-based surface heat fluxes from the Global Land Evaporation Amsterdam Model (GLEAM), FluxCom, and the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration (CAMELE). Given that SM variability typically resembles a first-order Markov process, this study introduces a methodology to estimate random errors in satellite-based SM data, improving the accuracy of LA coupling indices. One of the LA coupling indices is the corrected Pearson correlation coefficients between SM and surface fluxes. Additionally, Soil Moisture Metrics, such as corrected soil moisture memory and key breakpoints (e.g., wilting point, critical soil moisture), are applied to analyze regime distributions. The evaluation of corrected LA coupling metrics against in-situ measurements from the AmeriFlux network further enhances confidence in these estimates. Ultimately, this research provides a new benchmark for model validation, offering insights into potential improvements in climate model parameterizations and predictive capabilities.  
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Abstract

Soil moisture (SM) plays a crucial role in land-atmosphere (LA) interactions by regulating evapotranspiration and influencing moisture and energy budgets. However, challenges persist in quantifying LA coupling due to inconsistencies across observational datasets, reanalysis products, and model simulations. This study addresses these challenges by developing observationally derived LA coupling metrics while accounting for stochastic errors in satellite-based soil moisture measurements. These metrics are then applied to the Community Earth System Model (CESM) to provide a robust framework for model validation. Global observationally gridded LA coupling metrics are constructed using SM time series from the Soil Moisture Active Passive (SMAP L3) satellite, the Climate Change Initiative (ESA CCI v08.1), and machine learning-derived soil moisture (SoMo.ml), alongside observation-based surface heat fluxes from the Global Land Evaporation Amsterdam Model (GLEAM), FluxCom, and the Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration (CAMELE). Given that SM variability typically resembles a first-order Markov process, this study introduces a methodology to estimate random errors in satellite-based SM data, improving the accuracy of LA coupling indices. One of the LA coupling indices is the corrected Pearson correlation coefficients between SM and surface fluxes. Additionally, Soil Moisture Metrics, such as corrected soil moisture memory and key breakpoints (e.g., wilting point, critical soil moisture), are applied to analyze regime distributions. The evaluation of corrected LA coupling metrics against in-situ measurements from the AmeriFlux network further enhances confidence in these estimates. Ultimately, this research provides a new benchmark for model validation, offering insights into potential improvements in climate model parameterizations and predictive capabilities. Supplementary Material File (tavakoli_esom_2025.pptx) - Download - 13.72 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

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Authors Funding Information Metrics & Citations Metrics Article Usage 240views 129downloads Citations Download citation Nazanin Tavakoli, Paul Dirmeyer. A New Benchmark for Land-Atmosphere Coupling: Correcting Observational Metrics for Validating CESM-Derived Estimates. Authorea. 14 April 2025. DOI: https://doi.org/10.22541/au.174466002.25009692/v1 DOI: https://doi.org/10.22541/au.174466002.25009692/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

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