Performance of an empirical bias‐correction of a high‐resolution climate dataset

Abstract

We describe the method and performance of a bias-correction applied to high-resolution (10 km) simulations from a stretched-grid Regional Climate Model (RCM) over Tasmania, Australia. The bias-correction is a quantile mapping of empirical cumulative frequency distributions. Corrections are applied at a daily time step to five variables: rainfall, potential evaporation (PE), solar radiation, maximum temperature and minimum temperature. Corrections are calculated independently for each season.We show that quantile mapping of empirical distributions can be highly effective in correcting biases in RCM outputs. Cross-validation shows biases are effectively reduced across the range of cumulative frequency distributions, with few exceptions. The bias-correction is not as effective at correcting biases for values at or near zero (e.g. in rainfall simulations), although even here the bias-correction improves biases evident in the uncorrected simulations. In addition, the biascorrection improves frequency characteristics of variables such as the number of rain days.We use a detrending technique to apply the bias-correction to 140-year time series of RCM variables. We show that the bias-correction effectively preserves long-term changes (e.g. to the mean and variance) to variables projected by the uncorrected RCM simulations. Correlations between key variables are also largely preserved, thus the bias-corrected outputs reflect the dynamics of the underlying RCM. However, the bias-corrected simulations still exhibit some of the deficiencies of the RCM simulations, e.g. the tendency to underestimate the magnitude and duration of large, multi-day rain events, and the tendency to underestimate the duration of dry spells.The bias-corrected simulations for six downscaled GCMs for the A2 and B1 emissions scenarios are available to researchers from http://www.tpac.org.au.

Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China

“…However, despite the increase in resolution, downscaling simulation results (e.g. RCM) often remain too biased to be used directly in impact models such as hydrological models (Bennett et al, 2014). Therefore, to obtain a realistic output for hydrological simulations forced by future climate, certain statistical bias correction methodologies that involve particular forms of transfer function derived from cumulative distribution functions of observations and model simulations have been developed to produce corrected GCM/RCM simulations (e.g.…”

“…Therefore, to obtain a realistic output for hydrological simulations forced by future climate, certain statistical bias correction methodologies that involve particular forms of transfer function derived from cumulative distribution functions of observations and model simulations have been developed to produce corrected GCM/RCM simulations (e.g. Bennett et al, 2014;Li et al, 2010). Based on the data provided by GCMs, numerous studies have investigated the effects of climate change on regional floods over the world, including in Europe (Feyen et al, 2012), Germany (Huang et al, 2013), Bangladesh (Mirza et al, 2003), Britain (Kay and Jones, 2012), and China (e.g.…”

Beneficial impacts of climate change on pastoral and broadacre agriculture in cool-temperate Tasmania

“…Small biases in temperature or rainfall may significantly affect outputs from biophysical models. Biases can be managed by perturbing historical datasets with projected anomalies or by correcting the climate model outputs (Bennett et al 2013). The CFT project used the AWAP gridded daily dataset (Jones et al 2009) and a quantile-quantile bias-adjustment method (Bennett et al 2013) to produce climate variables on the same scale as observations.…”

“…Biases can be managed by perturbing historical datasets with projected anomalies or by correcting the climate model outputs (Bennett et al 2013). The CFT project used the AWAP gridded daily dataset (Jones et al 2009) and a quantile-quantile bias-adjustment method (Bennett et al 2013) to produce climate variables on the same scale as observations. Each climate model produces a unique realisation of the climate for each grid cell that is independent of observations, for example, rain falls on different days in different amounts in each model simulation.…”

“…Each climate model produces a unique realisation of the climate for each grid cell that is independent of observations, for example, rain falls on different days in different amounts in each model simulation. This also means that each climate realisation may not reflect observations such as the period Bennett et al (2013) showed that quantile mapping is very effective in correcting GCM biases. It improves frequency metrics such as the number of rain days, it preserves long-term changes to the mean and variance of climate variables to correlations between variables, and it reflects the dynamics (e.g.…”