ClimaLand: a land surface model designed to enable data-driven parameterizations

Deck, K., Braghiere, R. K., Renchon, A. A., Sloan, J., Bozzola, G., Speer, E., Mackay, J. B., Reddy, T., Phan, K., Gagné-Landmann, A. L., Li, Y., Yatunin, D., Charbonneau, A., Efrat-Henrici, N., Bach, E. ORCID: https://orcid.org/0000-0002-9725-0203, Ma, S., Gentine, P., Frankenberg, C., Bloom, A. A., Wang, Y., Longo, M. and Schneider, T. (2026) ClimaLand: a land surface model designed to enable data-driven parameterizations. Journal of Advances in Modeling Earth Systems, 18 (1). e2025MS005118. ISSN 1942-2466 doi: 10.1029/2025MS005118

Abstract/Summary

Land surface models (LSMs) are essential tools for simulating the coupled climate system, representing the dynamics of water, energy, and carbon fluxes on land and their interaction with the atmosphere. However, parameterizing sub-grid processes at the scales relevant to climate models ( 10–100 km) remains a considerable challenge. The parameterizations typically have a large number of unknown and often correlated parameters, making calibration and uncertainty quantification difficult. Moreover, many existing LSMs are not readily adaptable to the incorporation of modern machine learning (ML) parameterizations trained with in situ and satellite data. This article presents the first version of ClimaLand, a new LSM designed for overcoming these limitations, including a description of the core equations underlying the model, the results of an extensive set of validation exercises, and an assessment of the computational performance of the model. We show that ClimaLand can leverage graphics processing units for computational efficiency, and that its modular architecture and high-level programming language, Julia, allows for integration with ML libraries. In the future, this will enable efficient simulation, calibration, and uncertainty quantification with ClimaLand.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/127790
Identification Number/DOI	10.1029/2025MS005118
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE) Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Geophysical Union
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