aiLand v1: physics-based land surface emulator  with observational fine-tuning

Raoult, Nina; Pinnington, Ewan; Santa Cruz, Mario; Pinault, Florian; Raoult, Baudouin; Zelenka, Natalie; Arduini, Gabriele; Balsamo, Gianpaolo; Boussetta, Souhail; Chantry, Matthew; de Rosnay, Patricia; Dueben, Peter; Rüdiger, Christoph

doi:10.5194/gmd-19-10763-2026

Articles | Volume 19, issue 21

https://doi.org/10.5194/gmd-19-10763-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-10763-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 21

Model description paper

|

12 Nov 2026

Model description paper |

| 12 Nov 2026

aiLand v1: physics-based land surface emulator with observational fine-tuning

Nina Raoult, Ewan Pinnington, Mario Santa Cruz, Florian Pinault, Baudouin Raoult, Natalie Zelenka, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Matthew Chantry, Patricia de Rosnay, Peter Dueben, and Christoph Rüdiger

Data sets

aiLand v1: Physics-Based Land Surface Emulator with Observational Fine Tuning Nina Raoult et al. https://doi.org/10.21957/0f6t-7f73

aiLand v1: Physics-Based Land Surface Emulator with Observational Fine Tuning Nina Raoult et al. https://doi.org/10.21957/fs25-c406

FluxDataKit v3.4.2: A comprehensive data set of ecosystem fluxes for land surface modelling K. Hufkens and B. Stocker https://doi.org/10.5281/zenodo.14808331

Team and ICOS Ecosystem Thematic Centre: Warm Winter 2020 ecosystem eddy covariance flux product for 73 stations in FLUXNET-Archive format - release 2022-1 (Version 1.0) Warm Winter 2020 Team and ICOS Ecosystem Thematic Centre https://doi.org/10.18160/2G60-ZHAK

Model code and software

aiLand-code-base Nina Raoult and Ewan Pinnington https://doi.org/10.5281/zenodo.20764680

ecmwf-ifs/ecland: ecLand 2.0.0 Ahmad Nawab et al. https://doi.org/10.5281/zenodo.21995581

Short summary

The land surface shapes our weather and climate by controlling how heat and water move between the ground and the air, so the models representing it matter enormously. But they are costly to run and carry built-in errors. We built a faster artificial-intelligence version of one, then used real measurements from sites worldwide to correct some of these errors. The result stays reliable over years and better captures these heat and water exchanges; a quicker, more accurate tool for forecasting.