Preprints
https://doi.org/10.5194/egusphere-2023-2536
https://doi.org/10.5194/egusphere-2023-2536
02 Jan 2024
 | 02 Jan 2024

Bias Correction of Climate Models using a Bayesian Hierarchical Model

Jeremy Daniel Carter, Erick Chacón-Montalván, and Amber Leeson

Abstract. 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 individuals involved in climate change adaption strategies where accurate risk assessment is required for optimal resource allocation.

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Jeremy Daniel Carter, Erick Chacón-Montalván, and Amber Leeson

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2536', Anonymous Referee #1, 31 Jan 2024
  • RC2: 'Comment on egusphere-2023-2536', Anonymous Referee #2, 31 Jan 2024
  • RC3: 'Comment on egusphere-2023-2536', Anonymous Referee #3, 31 Jan 2024
  • AC1: 'Author's Comment (AC) on egusphere-2023-2536 in Reply to Referee Comments', Jeremy Carter, 25 Mar 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2536', Anonymous Referee #1, 31 Jan 2024
  • RC2: 'Comment on egusphere-2023-2536', Anonymous Referee #2, 31 Jan 2024
  • RC3: 'Comment on egusphere-2023-2536', Anonymous Referee #3, 31 Jan 2024
  • AC1: 'Author's Comment (AC) on egusphere-2023-2536 in Reply to Referee Comments', Jeremy Carter, 25 Mar 2024
Jeremy Daniel Carter, Erick Chacón-Montalván, and Amber Leeson

Data sets

Data used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J. Carter et. al J. Carter https://doi.org/10.5281/zenodo.10053531

Model code and software

Code used in generation of results in 'Bias Correction of Climate Models using a Bayesian Hierarchical Model' J. Carter et. al J. Carter https://doi.org/10.5281/zenodo.10053653

Jeremy Daniel Carter, Erick Chacón-Montalván, and Amber Leeson

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Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts for life on Earth. Although, the output is often afflicted with some bias. In this paper a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision making.