Exploring the potential of history matching for  land surface model calibration

Raoult, Nina; Beylat, Simon; Salter, James M.; Hourdin, Frédéric; Bastrikov, Vladislav; Ottlé, Catherine; Peylin, Philippe

doi:https://doi.org/10.5194/gmd-17-5779-2024

Articles | Volume 17, issue 15

https://doi.org/10.5194/gmd-17-5779-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-17-5779-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 15

Methods for assessment of models

|

01 Aug 2024

Methods for assessment of models |

| 01 Aug 2024

Exploring the potential of history matching for land surface model calibration

Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin

Data sets

simonbeylat/History_Matching_ORCHIDEE: v1.0.0 (Exp_Pot_HM) S. Beylat and N. Raoult https://doi.org/10.5281/zenodo.10592299

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (https://fluxnet.org/data/fluxnet2015-dataset/) G. Pastorello et al. https://doi.org/10.1038/s41597-020-0534-3

Model code and software

ORCHIDEE_v2_r5968 IPSL Data Catalogue https://doi.org/10.14768/d64cfc44-08b7-4384-aace-52e273685c09

Short summary

We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.