Diagnosing the representation of surface and layered soil moisture in Earth system models

Abstract Surface soil moisture ( mrsos ) and vertically integrated soil moisture ( mrsol ) over the top 10 cm should, by definition, be physically consistent in Earth System Models (ESMs). However, an evaluation of nine CMIP6 models reveals substantial inconsistencies: in some models, mrsos and integrated mrsol agree globally; in others, they align only in specific regions; and in a few, they diverge across all grid cells. These discrepancies arise from a combination of factors, including metadata errors, inconsistent variable definitions, or diagnostic sequencing within the model. We demonstrate how such issues can lead to significant biases, even when both variables are present and seemingly well-defined. As model complexity increases and multi-model comparisons become more common, assumptions about variable equivalence may lead to flawed conclusions. This study highlights the need for routine consistency checks, improved metadata standards, and community-wide practices that ensure reliability of derived variables across ESM outputs, particularly in preparation for CMIP7.

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Publication Details

Journal
Environmental Research Letters
Published
2025-12-19
DOI
https://doi.org/10.1088/1748-9326/ae2f72
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
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article

Diagnosing the representation of surface and layered soil moisture in Earth system models

Elias Massoud, Nathaniel O. Collier, Mingjie Shi, Min Xu et al.
Environmental Research Letters
Soil Moisture and Remote Sensing
article

Diagnosing the representation of surface and layered soil moisture in Earth system models

Elias Massoud, Nathaniel O. Collier, Mingjie Shi, Min Xu, Forrest M. Hoffman
article en

Abstract

Abstract Surface soil moisture ( mrsos ) and vertically integrated soil moisture ( mrsol ) over the top 10 cm should, by definition, be physically consistent in Earth System Models (ESMs). However, an evaluation of nine CMIP6 models reveals substantial inconsistencies: in some models, mrsos and integrated mrsol agree globally; in others, they align only in specific regions; and in a few, they diverge across all grid cells. These discrepancies arise from a combination of factors, including metadata errors, inconsistent variable definitions, or diagnostic sequencing within the model. We demonstrate how such issues can lead to significant biases, even when both variables are present and seemingly well-defined. As model complexity increases and multi-model comparisons become more common, assumptions about variable equivalence may lead to flawed conclusions. This study highlights the need for routine consistency checks, improved metadata standards, and community-wide practices that ensure reliability of derived variables across ESM outputs, particularly in preparation for CMIP7.

Environmental Research LettersVol. 21(2)
Oak Ridge National Laboratory (US), Battelle (US), Pacific Northwest National Laboratory (US)
Openalex Percentile: Top 60%
Soil Moisture and Remote Sensing
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