Abstract
Abstract. With the increase in the number of available global climate models (GCMs), pragmatic questions come up in using them to quantify climate change impacts on hydrology: is it necessary to unequally weight GCM outputs in the impact studies, and if so, how should they be weighted? Some weighting methods have been proposed based on the performances of GCM simulations with respect to reproducing the observed climate. However, the process from climate variables to hydrological responses is nonlinear, and thus the assigned weights based on performances of GCMs in climate simulations may not be correctly translated to hydrological responses. Assigning weights to GCM outputs based on their ability to represent hydrological simulations is more straightforward. Accordingly, the present study assigns weights to GCM simulations based on their ability to reproduce hydrological characteristics and investigates their influences on the quantification of hydrological impacts. Specifically, eight weighting schemes are used to determine the weights of GCM simulations based on streamflow series simulated by a lumped hydrological model using raw or bias-corrected GCM outputs. The impacts of weighting GCM simulations are investigated in terms of reproducing the observed hydrological regimes for the reference period (1970–1999) and quantifying the uncertainty of hydrological changes for the future period (2070–2099). The results show that when using raw GCM outputs to simulate streamflows, streamflow-based weights have a better performance in reproducing observed mean hydrograph than climate-variable-based weights. However, when bias correction is applied to GCM simulations before driving the hydrological model, the streamflow-based unequal weights do not bring significant differences in the multi-model ensemble mean and uncertainty of hydrological impacts, since bias-corrected climate simulations become rather close to observations. Thus, it is likely that using bias correction and equal weighting is viable and sufficient for hydrological impact studies.
Highlights
Multi-model ensembles (MMEs) consisting of climate simulations from multiple global climate models (GCMs) have been widely used to quantify future climate change impacts and the corresponding uncertainty (Wilby and Harris, 2006; IPCC, 2013; Chiew et al, 2009; Chen et al, 2011; Tebaldi and Knutti, 2007)
The RAC method generates less differentiated unequal weights, followed by the Bayesian model averaging (BMA) and performance and interdependence skill (PI) methods, but weights assigned by the probability density function (PDF) method closely resemble the equal-weighting method
When weights are calculated based on bias-corrected GCM-simulated streamflows, the inequality of weights is reduced, and all the unequalweighting methods receive a lower entropy of weights for both watersheds (Table 3)
Summary
Multi-model ensembles (MMEs) consisting of climate simulations from multiple global climate models (GCMs) have been widely used to quantify future climate change impacts and the corresponding uncertainty (Wilby and Harris, 2006; IPCC, 2013; Chiew et al, 2009; Chen et al, 2011; Tebaldi and Knutti, 2007). Due to the lack of consensus on the proper way to combine simulations of an MME, the prevailing approach is the model democracy (“one model one vote”) for the sake of simplicity, where each member in an ensemble is considered to have equal ability in simulating historical and future climates. The model democracy method has been applied to many global and regional climate change impact studies (e.g., IPCC, 2014; Minville et al, 2008; Maurer, 2007). It has been reported that the equal average of an MME often outperforms any individual model in regards to the reproduction of the mean state of observed historical climate (Gleckler et al, 2008; Reichler and Kim, 2008), whether the equal weighting is a better strategy for Published by Copernicus Publications on behalf of the European Geosciences Union
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