## 1. Introduction

[2] Managing future fresh water resources under a changing climate with vastly uncertain future atmospheric greenhouse gas emission scenarios is a daunting challenge facing human society today. Hydrological models are one of the most powerful tools at our disposal to address that challenge. Hydrological simulations of scenario projections require adequate projected fields of meteorological forcing variables such as precipitation and temperature. These cannot be directly derived from global climate model simulations of future climate, which are significantly affected by errors, since the results from such a forced hydrological simulation would be unrealistic and of little use [*Hansen et al.*, 2006; *Sharma et al.*, 2007]. Hydrological models are developed to give realistic output when forced with observed fields. Hence some form of post processing on GCM forcing fields is necessary. Removal of the bias, defined here as the time-independent component of the error, is a widely used form of post processing.

[3] *Hagemann et al.* [2011] weighted the benefits of bias correction against the apparent addition of uncertainty in the resulting simulated future hydrological fields due to the bias correction parameter choice. Bias correction of projected scenario forcing fields is known to affect not only the projected hydrological fields, but also the climate change signal [*Haerter et al.*, 2011]. Policy makers and fresh water resource strategists require the best possible information regarding hydrological modeling of projected climate. This entails not only the best possible estimate of future hydrological fields, but also the best possible estimate of the associated uncertainties.

[4] In this work we compare the uncertainty, as estimated by spread, in hydrological variables from simulations of projected climates from three different sources: the choice of GCM to force the hydrological simulations, the choice of SRES used to determine the atmospheric greenhouse gas concentrations and the choice of decade used to derive the bias correction parameters. The choice of decade used for the bias correction stands in lieu of a full, and far more cumbersome, analysis of the uncertainty associated with bias correction. This simplification stems from the finding of *Piani et al.* [2010b] that the choice of decade was by far the largest contributor to the uncertainty in this particular bias correction methodology, when compared to other factors such as fit error, choice of transfer function and observational uncertainty. *Piani et al.* [2010a, 2010b] chose the decadal time scale, given the total length of observation available (40 years), as a compromise between the need for a large number of non overlapping time periods and the need for these periods to be as long as possible. We accept that the choice of any one particular bias correction methodology may be another source of uncertainty. However a comparative analysis of all bias correction methodologies is beyond the scope of this paper. In the following section 2 the experiment is presented: We describe the models used, the length and boundary conditions of the simulations performed and key references of the bias correction method applied. Section 3 contains the results of the experiments. In section 4 we discuss the results, their limitations and implications and conclude this work. The auxiliary material provides details of our methodology for calculating the spread attributable to a single source and using it to estimate the associated uncertainty.