Validation of a CDF-t bias correction method using palaeo-data for the Mid-Holocene and the Last Glacial Maximum

<p>The main objective of this study is to develop and test a method of bias correction for paleoclimate model simulations using the &#8220;Cumulative Distribution Functions &#8211; transform&#8221; (CDF-t) method. The CDF-t is a quantile-mapping based method, extended to account for climate change signal. Here we apply the CDF-t to climate model outputs for the Mid-Holocene and the Last Glacial Maximum, simulated by the climate model of intermediate complexity iLOVECLIM at 5.625&#176; resolution. Additionally, we test the proposed methodology on iLOVECLIM model outputs dynamically downscaled on a&#160; 0.25&#176; resolution.</p><p>The results are validated through inverse and forward modelling approaches. The inverse approach implies comparing the obtained results with proxy-based reconstructed climatic variables. Here we use temperature and precipitation reconstructions, obtained with inverse modelling methods from pollen data. In this study, both gridded and point-based multi-proxy reconstruction datasets were used for the analysis.</p><p>The forward approach includes a further step of vegetation modelling, using the climatologies derived from bias-corrected outputs of the iLOVECLIM model in CARAIB (CARbon Assimilation In the Biosphere) global dynamic vegetation model. The modelled biomes are evaluated in comparison with pollen-based biome reconstructions BIOME6000.</p><p>The findings of this study indicate that the use of the proposed methodology results in significant improvements in climate and vegetation modelling and suggest that the CDF-t method is an valuable approach to reduce biases in paleoclimate modelling.</p>