Abstract
AbstractSystematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions.
Highlights
Bias adjustment (BA) techniques are routinely applied in sectoral impact studies to calibrate the required global and regional model outputs to regional or local scale, using a particular gridded or point-scale observational reference (Rojas et al, 2012, Barredo et al, 2016, Ruiz-Ramos et al, 2016, Casanueva et al, 2018, Reder et al, 2018, Galmarini et al, 2019)
Two main sources of uncertainty which may largely influence the results of these methods are (a) observational uncertainty and (b) resolution mismatch
Quantile delta mapping (QDM): Empirical method divided in three steps (i) future model outputs are detrended by quantile; (ii) quantile mapping is applied to all empirical detrended quantiles of the detrended series; (iii) the projected trends are reapplied to the bias-adjusted quantiles
Summary
Bias adjustment (BA) techniques are routinely applied in sectoral impact studies to calibrate the required (biased) global and regional model outputs to regional or local scale, using a particular gridded or point-scale observational reference (Rojas et al, 2012, Barredo et al, 2016, Ruiz-Ramos et al, 2016, Casanueva et al, 2018, Reder et al, 2018, Galmarini et al, 2019). The aim is to assess (a) how the different methods may alter the raw climate change signal (of both global and regional model outputs), and (b) the influence that the observational uncertainty and the resolution mismatch may have on the results. Note that these techniques adjust the (biased) model values towards the corresponding observed ones and this may indirectly affect the trends and the resulting climate change signal (Maraun, 2013). Note that a warm or dry spell has been defined as at least two consecutive days fulfilling a particular condition
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