Parameter estimation in land surface models: challenges and opportunities with data assimilation and machine learning

Raoult, Nina ORCID: https://orcid.org/0000-0003-2907-9456; Douglas, Natalie ORCID: https://orcid.org/0000-0002-3404-8761; MacBean, Natasha ORCID: https://orcid.org/0000-0001-6797-4836; Kolassa, Jana ORCID: https://orcid.org/0000-0001-6644-8789; Quaife, Tristan ORCID: https://orcid.org/0000-0001-6896-4613; Roberts, Andrew G. ORCID: https://orcid.org/0009-0002-4274-7914; Fisher, Rosie ORCID: https://orcid.org/0000-0003-3260-9227; Fer, Istem; Bacour, Cédric ORCID: https://orcid.org/0000-0002-1913-3722; Dagon, Katherine ORCID: https://orcid.org/0000-0002-4518-8225; Hawkins, Linnia; Carvalhais, Nuno ORCID: https://orcid.org/0000-0003-0465-1436; Cooper, Elizabeth ORCID: https://orcid.org/0000-0002-1575-4222; Dietze, Michael C.; Gentine, Pierre ORCID: https://orcid.org/0000-0002-0845-8345; Kaminski, Thomas; Kennedy, Daniel ORCID: https://orcid.org/0000-0001-9494-3509; Liddy, Hannah M. ORCID: https://orcid.org/0000-0002-8666-0805; Moore, David J.P. ORCID: https://orcid.org/0000-0002-6462-3288; Peylin, Philippe ORCID: https://orcid.org/0000-0001-9335-6994; Pinnington, Ewan; Sanderson, Benjamin; Scholze, Marko ORCID: https://orcid.org/0000-0002-3474-5938; Seiler, Christian ORCID: https://orcid.org/0000-0002-2092-0168; Smallman, T. Luke ORCID: https://orcid.org/0000-0002-0835-1003; Vergopolan, Noemi ORCID: https://orcid.org/0000-0002-7298-0509; Viskari, Toni ORCID: https://orcid.org/0000-0002-3357-1374; Williams, Mathew ORCID: https://orcid.org/0000-0001-6117-5208; Zobitz, John ORCID: https://orcid.org/0000-0002-1830-143X. 2025 Parameter estimation in land surface models: challenges and opportunities with data assimilation and machine learning. Journal of Advances in Modeling Earth Systems, 17 (11), e2024MS004733. 42, pp. 10.1029/2024MS004733

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Abstract/Summary

Accurately predicting terrestrial ecosystem responses to climate change over long-timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimization, focusing on carbon-water-vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterizing errors due to observation biases and model-data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple data sets, and including the spin-up and historical period in the assimilation window. We outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation data sets, harness ML advances, and enhance the predictive capabilities of LSMs.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1029/2024MS004733
UKCEH and CEH Sections/Science Areas:	Water and Climate Science (2025-)
ISSN:	1942-2466
Additional Information:	Open Access paper - full text available via Official URL link.
Additional Keywords:	land surface modeling, parameter estimation, data assimilation, uncertainty quantification, model calibration, machine learning
NORA Subject Terms:	Earth Sciences Hydrology Meteorology and Climatology
Date made live:	30 Oct 2025 13:44 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/540472

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