Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain - N. Reyniers; Q. Zha; N. Addor; N. Addor; T. J. Osborn; T. J. Osborn; N. Forstenhäusler; Y. He (Earth System Science Data)
The United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km regional perturbed physics ensemble (UKCP18-RCM-PPE) is one of the three strands of the latest set of UK national climate projections produced by the UK Met Office. It has been widely adopted in climate impact assessment. In this study, we report biases in the raw UKCP18-RCM simulations that are significant and are likely to deteriorate impact assessments if they are not adjusted. Two methods were used to bias-correct UKCP18-RCM: non-parametric quantile mapping using empirical quantiles and a variant developed for the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) designed to preserve the climate change signal. Specifically, daily temperature and precipitation simulations for 1981 to 2080 were adjusted for the 12 ensemble members. Potential evapotranspiration was also estimated over the same period using the Penman–Monteith formulation and then bias-corrected using the latter method. Both methods successfully corrected biases in a range of daily temperature, precipitation, and potential evapotranspiration metrics and reduced biases in multi-day precipitation metrics to a lesser degree. An exploratory analysis of the projected future changes confirms the expectation of wetter, warmer winters and hotter, drier summers and shows uneven changes in different parts of the distributions of both temperature and precipitation. Both bias-correction methods preserved the climate change signal almost equally well, as well as the spread among the projected changes. The change factor method was used as a benchmark for precipitation, and we show that it fails to capture changes in a range of variables, making it inadequate for most impact assessments. By comparing the differences between the two bias-correction methods and within the 12 ensemble members, we show that the uncertainty in future precipitation and temperature changes stemming from the climate model parameterization far outweighs the uncertainty introduced by selecting one of these two bias-correction methods. We conclude by providing guidance on the use of the bias-corrected datasets. The datasets bias-adjusted with ISIMIP3BA are publicly available in the following repositories: https://doi.org/10.5281/zenodo.6337381 for precipitation and temperature (Reyniers et al., 2022a) and https://doi.org/10.5281/zenodo.6320707 for potential evapotranspiration (Reyniers et al., 2022b). The datasets, bias-corrected using the quantile mapping method, are available at https://doi.org/10.5281/zenodo.8223024 (Zha et al., 2023).