A Comprehensive Assessment of Climate Data Bias-Adjustment Techniques Over Australia

The National Partnership for Climate Projections (NPCP) aims to develop a consistent approach to deliver comparable, robust, fit-for-purpose future climate information to assess climate risks and inform adaptation planning. The NPCP climate projections roadmap identifies a number of priority areas of collaboration, including the delivery of national and regional downscaled climate projections. This involves selecting global climate models (GCMs), downscaling using regional climate models (RCMs), bias-adjusting model outputs, and conducting secondary and next-level analysis (e.g., impact modelling).

The focus on bias-adjustment is an acknowledgement of the fact that GCM and RCM outputs often show significant discrepancies when compared to observations. These systematic errors, or biases, can render raw outputs unsuitable for direct use in downstream impact models such as those for hydrology and agriculture, as well as in climate risk assessments. For the NPCP bias-adjustment intercomparison project, we evaluated various bias-adjustment techniques currently in use in the Australian climate research community. These include Equi-distant/ratio Cumulative Density Function matching (ECDFm), Quantile Matching for Extremes (QME), N-Dimensional Multivariate Bias Correction (MBCn), and Multivariate Recursive Nesting Bias Correction (MRNBC).

While previous studies have assessed some of these techniques for specific metrics and applications in Australia, our evaluation aimed to be broad and comprehensive. The participating techniques were applied to daily RCM data from the CORDEX-CMIP6 project for a baseline task, where bias-adjusted data were produced for the 1980-2019 period using 1980-2019 as a training period, as well as a cross-validation task, where data were produced for 1990-2019 using 1960-1989 for training. These bias-adjusted data were then compared to observations across Australia on various metrics relating to temperature and precipitation climatology, variability, statistical distribution and extremes. The impact of bias-adjustment on simulated trends was also assessed by producing bias-adjusted data for the 2060-2099 period. Additionally, we compared the bias-adjustment techniques with a simple quantile delta change approach and investigated scenarios where it may be sufficient to directly bias-adjust GCM data without the need for computationally expensive downscaling.

Based on the results of the intercomparison, the best-performing techniques were subsequently used by the Australian Climate Service (ACS) to bias-adjust outputs from the CORDEX-CMIP6 archive. This ensures the availability of a consistent set of high-resolution, bias-adjusted products for the Australian community to evaluate climate hazards and risks, and support adaptation planning.