The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Marko Scholze

Senior lecturer

Default user image.

Parameter Estimation in Land Surface Models : Challenges and Opportunities With Data Assimilation and Machine Learning

Author

  • Nina Raoult
  • Natalie Douglas
  • Natasha MacBean
  • Jana Kolassa
  • Tristan Quaife
  • Andrew G. Roberts
  • Rosie Fisher
  • Istem Fer
  • Cédric Bacour
  • Katherine Dagon
  • Linnia Hawkins
  • Nuno Carvalhais
  • Elizabeth Cooper
  • Michael C. Dietze
  • Pierre Gentine
  • Thomas Kaminski
  • Daniel Kennedy
  • Hannah M. Liddy
  • David J.P. Moore
  • Philippe Peylin
  • Ewan Pinnington
  • Benjamin Sanderson
  • Marko Scholze
  • Christian Seiler
  • T. Luke Smallman
  • Noemi Vergopolan
  • Toni Viskari
  • Mathew Williams
  • John Zobitz
Show all

Summary, in English

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.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • eSSENCE: The e-Science Collaboration
  • MERGE: ModElling the Regional and Global Earth system

Publishing year

2025

Language

English

Publication/Series

Journal of Advances in Modeling Earth Systems

Volume

17

Issue

11

Document type

Review article

Publisher

Wiley-Blackwell

Topic

  • Physical Geography

Keywords

  • data assimilation
  • land surface modeling
  • machine learning
  • model calibration
  • parameter estimation
  • uncertainty quantification
  • SDG 13 - Climate Action
  • SDG 15 - Life on Land

Status

Published

ISBN/ISSN/Other

  • ISSN: 1942-2466