An evaluation framework for downscaling and bias correction in climate change impact studies

• DOI: 10.1016/j.jhydrol.2023.129693
• Publication Date: 2023-05-30T06:02:30Z
• Authors: Elisabeth Vogel; Fiona Johnson; Lucy Marshall; Ulrike Bende-Michl; Louise Wilson; Justin R. Peter; Conrad Wasko; Sri Srikanthan; Wendy Sharples; Andrew Dowdy; Pandora Hope; Zaved Khan; Raj Mehrotra; Ashish Sharma; Vjekoslav Matic; Alison Oke; Margot Turner; Steven Thomas; Chantal Donnelly; Vi Co Duong
• Keywords: Impact assessment

Climate change impact studies commonly use models (such as hydrological or crop models) forced with corrected climate input data from global models. A range of downscaling and bias correction methods have been developed to increase the spatial resolution remove systematic biases in model outputs be applied before Many focused on evaluating such approaches for variables they aim correct. However, due nonlinear error propagation there can large remaining outputs, even when ingesting forcings. Here we propose an impact-centric evaluation framework used risk assessments. This evaluates compares strengths limitations domain, highlighting that lead reduced interest. We demonstrate context assessing projections Australia. Our results show although all evaluated perform adequately variables, their errors vary markedly is modelled. proposed involves selecting a number key performance metrics, ranking four compute overall ranking, best-performing each statistical metric approach. present application this approach using metrics relevant applications, relating mean biases, variability, heavy precipitation peak runoff days, dry conditions. For related find multi-variate considers cross-correlations, temporal auto-correlations at multiple time scales (daily annual) performs best reducing output wide applications where are required, including impacts agricultural production, wildfires, energy generation, human health, ecosystem functioning, water resource management.