An empirical evaluation of bias correction methods for palaeoclimate simulations

Abstract

Even the most sophisticated global climate models are known to have significant biases in the way they simulate the climate system. Correcting model biases is therefore an essential step towards realistic palaeoclimatologies, which are important for many applications such as modelling long-term ecological dynamics. Here, we evaluate three widely used bias correction methods – the delta method, generalised additive models (GAMs), and quantile mapping – against a large global dataset of empirical temperature and precipitation records from the present, the mid-Holocene (∼ 6000 years BP), the Last Glacial Maximum (∼21 000 years BP), and the last interglacial period (∼125 000 years BP). In most cases, the differences between the bias reductions achieved by the three methods are small. Overall, the delta method performs slightly better, albeit not always to a statistically significant degree, at minimising the median absolute bias between empirical data and debiased simulations for both temperature and precipitation than GAMs and quantile mapping; however, there is considerable spatial and temporal variation in the performance of each of the three methods. Our data also indicate that it could soon be possible to use empirical reconstructions of past climatic conditions not only for the evaluation of bias correction methods but for fitting statistical relationships between empirical and simulated data through time that can inform more effective bias correction methods.