HESS Opinions "Should we apply bias correction to global and regional climate model data?"

Abstract

Despite considerable progress in recent years, output of both Global and Regional Circulation Models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem bias correction (BC), i.e. the correction of model output towards observations in a post processing step for its subsequent application in climate change impact studies has now become a standard procedure. In this paper we argue that bias correction, which has a considerable influence on the results of impact studies, is not a valid procedure in the way it is currently used: it impairs the advantages of Circulation Models which are based on established physical laws by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Bias correction largely neglects feedback mechanisms and it is unclear whether bias correction methods are time-invariant under climate change conditions. Applying bias correction increases agreement of Climate Model output with observations in hind casts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this masks rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. <br><br> We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of Circulation Models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future Global and Regional Circulation Model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated approaches for ensemble perturbation. <br><br> With this article, we advocate communicating the entire uncertainty range associated with climate change predictions openly and hope to stimulate a lively discussion on bias correction among the atmospheric and hydrological community and end users of climate change impact studies

Future Climate Change Impacts on Streamflows of Two Main West Africa River Basins: Senegal and Gambia

“…GCM have great difficulties taking into account regional heterogeneities of variability and changes of climate, according to Biao [5]. Therefore, these models may not be suitable to produce climate projections at regional, national, and local scales, which are necessary to assess the impacts of climate change and to develop adaptation policies [52]. In recent years, some initiatives, like the Coordinated Regional Climate Downscaling Experiment (CORDEX AFRICA), have been implemented to produce climate-projected variables at regional scales from Regional Climate Models (RCM) forced by GCM outputs in the West Africa region with a spatial resolution of 50 km.…”

A watershed-scale study of climate change impacts on groundwater recharge (Annapolis Valley, Nova Scotia, Canada)

“…Anandhi et al 2011, Stoll et al 2011, Sulis et al 2012. Assessments of different techniques and their limitations can be found in Hagemann et al (2011) and Ehret et al (2012). The five "delta change" climate scenarios for the future period were used to drive the infiltration model HELP.…”

Bias corrections of global models for regional climate simulations of high-impact weather

“…Dosio and Paruolo 2011). This approach suffers from the same limitations as the aforementioned statistical bias correction of GCMs and has the additional complication that GCM biases may irretrievably change-or even destroy-the high-impact weather signal of interest (Ehret et al 2012;Done et al 2013). …”

“…Unfortunately, biases that may be acceptable at global scales can be problematic for these downscaling applications to regional and extreme weather climate scales (e.g. Liang et al 2008;Ehret et al 2012;Xu and Yang 2012;Done et al 2013).…”