ClimaLand: A Land Surface Model Designed to Enable Data-Driven Parameterizations

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.

This research was supported by Schmidt Sciences, LLC, and by the Resnick Sustainability Institute at Caltech. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. We thank Lee Barbour and Mingbin Huang for providing the SV62 site data for the layered soil experiment, Dani Or and Peter Lehmann for providing the bare soil evaporation data, Yongjiu Dai and Nan Wei for helping us to understand the SoilGrids data, and Natan Holtzmann for aiding our initial flux tower simulations. We would also like to thank Elias Massoud, John Worden, Alex Norton, Victoria Meyer and Paul Levine for early discussions regarding ClimaLand. We acknowledge high-performance computing support from Caltech's Resnick High Performance Computing Center and the Derecho system (Computational and Information Systems Laboratory, 2023) provided by the National Center for Atmospheric Research (NCAR), sponsored by the National Science Foundation.