Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

B Thrasher,E P Maurer,C Mckellar,P B Duffy

doi:10.5194/hess-16-3309-2012

Abstract

Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

Highlights

While monthly, seasonal, and annual changes in climate have the potential to affect ecosystems and human development (e.g., Fowler and Kilsby, 2003; Palmer and Raisanen, 2002; Schneider et al, 2007), there has been an increasing interest in the effect of shorter-term extreme events (Christensen et al, 2007; IPCC, 2011)
Before any downscaled data can be ingested into a model to estimate specific impacts of climate change, some adjustment to account for the global climate model (GCM) biases must be included, since at least some of the bias is systematic, being induced by factors such as inadequate terrain resolution in the GCM (Haerter et al, 2011)
The quantile mapping approach has the benefit of accounting for GCM biases in all statistical moments, though, like all statistical downscaling approaches, it is assumed that biases relative to historic observations will be constant during the projections

Summary

Introduction

Seasonal, and annual changes in climate have the potential to affect ecosystems and human development (e.g., Fowler and Kilsby, 2003; Palmer and Raisanen, 2002; Schneider et al, 2007), there has been an increasing interest in the effect of shorter-term extreme events (Christensen et al, 2007; IPCC, 2011). The quantile mapping approach has the benefit of accounting for GCM biases in all statistical moments, though, like all statistical downscaling approaches, it is assumed that biases relative to historic observations will be constant during the projections. While this quantile mapping approach has been used extensively for downscaling monthly average precipitation and temperature (Hayhoe et al, 2008; Maurer and Duffy, 2005; Wood et al, 2004), its adaptation to daily data is relatively new (Abatzoglou and Brown, 2012; Maurer et al, 2010).

Methods

Results

Conclusion