Bias Correction of Climate Models using a Bayesian Hierarchical Model

• Main Authors: Carter, Jeremy Daniel, Chacón-Montalván, Erick, Leeson, Amber
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: article; Verlagsveröffentlichung; Antarc*; Antarctica
• Online Access: https://doi.org/10.5194/egusphere-2023-2536; https://noa.gwlb.de/receive/cop_mods_00070803; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00069133/egusphere-2023-2536.pdf; https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2536/egusphere-2023-2536.pdf

Climate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts on the biosphere. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently employed within the environmental sciences community, although the output can be afflicted with bias that impedes direct interpretation. Bias correction approaches using observational data aim to address this challenge. However, approaches are typically criticised for not being physically justified and not considering uncertainty in the correction. These aspects are particularly important in cases where observations are sparse, such as for weather station data over Antarctica. This paper attempts to address both of these issues through the development of a novel Bayesian hierarchical model for bias prediction. The model propagates uncertainty robustly and uses latent Gaussian process distributions to capture underlying spatial covariance patterns, partially preserving the covariance structure from the climate model which is based on well-established physical laws. The Bayesian framework can handle complex modelling structures and provides an approach that is flexible and adaptable to specific areas of application, even increasing the scope of the work to data assimilation tasks more generally. Results in this paper are presented for one-dimensional simulated examples for clarity, although the method implementation has been developed to also work on multidimensional data as found in most real applications. Performance under different simulated scenarios is examined, with the method providing most value added over alternative approaches in the case of sparse observations and smooth underlying bias. A major benefit of the model is the robust propagation of uncertainty, which is of key importance to a range of stakeholders, from climate scientists engaged in impact studies, decision makers trying to understand the likelihood of particular scenarios and ...