Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon

• Published in: Geoscientific Model Development
• Main Authors: M. Falls, R. Bernardello, M. Castrillo, M. Acosta, J. Llort, M. Galí
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: Geology; QE1-996.5; North Atlantic
• Online Access: https://doi.org/10.5194/gmd-15-5713-2022; https://doaj.org/article/eba9ce25a8f44b1e923e1d5c0fb9e91f

When working with Earth system models, a considerable challenge that arises is the need to establish the set of parameter values that ensure the optimal model performance in terms of how they reflect real-world observed data. Given that each additional parameter under investigation increases the dimensional space of the problem by one, simple brute-force sensitivity tests can quickly become too computationally strenuous. In addition, the complexity of the model and interactions between parameters mean that testing them on an individual basis has the potential to miss key information. In this work, we address these challenges by developing a biased random key genetic algorithm (BRKGA) able to estimate model parameters. This method is tested using the one-dimensional configuration of PISCES-v2_RC, the biogeochemical component of NEMO4 v4.0.1 (Nucleus for European Modelling of the Ocean version 4), a global ocean model. A test case of particulate organic carbon (POC) in the North Atlantic down to 1000 m depth is examined, using observed data obtained from autonomous biogeochemical Argo floats. In this case, two sets of tests are run, namely one where each of the model outputs are compared to the model outputs with default settings and another where they are compared with three sets of observed data from their respective regions, which is followed by a cross-reference of the results. The results of these analyses provide evidence that this approach is robust and consistent and also that it provides an indication of the sensitivity of parameters on variables of interest. Given the deviation in the optimal set of parameters from the default, further analyses using observed data in other locations are recommended to establish the validity of the results obtained.