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

Abstract

All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the sixhourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.

Peru‐Chile upwelling dynamics under climate change

“…First, the atmospheric forcing was built by adding model fields from climate projections and observational climatologies (SCOW for winds and COADS for heat fluxes) as in Bruyere et al [2014], assuming that the bias will remain identical under climate change conditions. Thus, the seasonal anomalies are not consistent with the mean state of the forcing.…”

“…20C3M, PI, 2CO2, and 4CO2 correspond to IPSL-CM4 climate scenarios with atmospheric CO 2 concentrations following, respectively, observed levels, fixed preindustrial levels, doubling, and quadrupling trends. the same methodology as Bruyere et al [2014]. Even though there is little alternative to our approach, it must be noticed that a bias in the mean state could result in a bias in the variance.…”

Dynamical downscaling with the fifth-generation Canadian regional climate model (CRCM5) over the CORDEX Arctic domain: effect of large-scale spectral nudging and of empirical correction of sea-surface temperature

“…Richter 2015). Given the sensitivity of RCMs simulations to the biases of the driving data, several attempts have been made to develop bias-correction methods, such as the studies of Christensen and Christensen (2007), Katzfey et al (2009), Bruyère et al (2014, Yu andHernández-Díaz et al (2016;hereinafter HD16), to cite but a few.…”

3-Step dynamical downscaling with empirical correction of sea-surface conditions: application to a CORDEX Africa simulation

“…Some of these correct the mean bias of the CGCM (Bruyère et al 2014;Done et al 2015), others corrects also the CGCM variance Yang 2012, 2015). In some other studies, climatechange delta of SST and atmospheric fields from a given CGCM simulation are applied as perturbation to the current-climate reanalysis data, and then these fields are used as BC to drive the RCM simulation for a future time-slice period (Patricola and Cook 2010;Yu and Wang 2014).…”

“…In some other studies, climatechange delta of SST and atmospheric fields from a given CGCM simulation are applied as perturbation to the current-climate reanalysis data, and then these fields are used as BC to drive the RCM simulation for a future time-slice period (Patricola and Cook 2010;Yu and Wang 2014). Bruyère et al (2014) corrected the mean bias of the CGCM while retaining its synoptic and climate variability by first decomposing the CGCM-simulated data as well as the atmospheric and SST reanalyses into a mean seasonally-varying climatological component and a perturbation component, and then constructing the BC for driving the RCM simulation by replacing the CGCM climatological mean component by that of the reanalyses. Yu and Wang (2014) did several experiments in which bias correction was applied to different sets of BC, containing synoptic forcing, monthly climatology with and without diurnal cycle.…”