Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models

SummaryA statistical bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures. The bias correction is based on a fitted histogram equalization function. This function is defined daily, as opposed to earlier published versions in which they were derived yearly or seasonally at best, while conserving properties of robustness and eliminating unrealistic jumps at seasonal or monthly transitions. The methodology is tested using the newly available global dataset of observed hydrological forcing data of the last 50 years from the EU project WATCH (WATer and global CHange) and an initial conditions ensemble of simulations performed with the ECHAM5 global climate model for the same period. Bias corrections are derived from 1960 to 1969 observed and simulated data and then applied to 1990–1999 simulations. Results confirm the effectiveness of the methodology for all tested variables. Bias corrections are also derived using three other non-overlapping decades from 1970 to 1999 and all members of the ECHAM5 initial conditions ensemble. A methodology is proposed to use the resulting “ensemble of bias corrections” to quantify the error in simulated scenario projections of components of the hydrological cycle.

Research highlights
► This study presents a statistical bias correction methodology that allows climate model output temperature and precipitation to be used as forcing for impact models.
► The methodology is tested with a state-of-the–art climate model and observational dataset.
► A methodology is also presented to keep track of the uncertainty associated with the statistical bias correction process.