Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study

Abstract

Climate simulations often need to be adjusted before carrying out climate impact studies at regional scale in order to reduce the biases often present in climate models. To do that, bias adjustment methods are usually applied to climate output simulations and are calibrated over a reference period. This period ideally includes good observational coverage and is often defined as the 2 or 3 more recent decades. However, on these timescales, the climate state may be influenced by the low-frequency internal climate variability. There is therefore a risk of introducing a bias to the climate projections by bias-adjusting simulations with low-frequency variability in a different phase to that of the observations. We proposed here a new pseudo-reality framework using an ensemble of simulations performed with the IPSL-CM6A-LR climate model in order to assess the impact of the low-frequency internal climate variability of the North Atlantic sea surface temperatures on bias-adjusted projections of mean and extreme surface temperature over Europe. We show that adjusting a simulation in a similar phase of the Atlantic Multidecadal Variability to that of the pseudo-observations reduces the pseudo-biases in temperature projections. Therefore, for models and regions where low frequency internal variability matters, it is recommended to sample relevant climate simulations to be bias adjusted in a model ensemble or alternatively to use a very long reference period when possible.