ORCID Profile
0000-0003-2945-8579
Current Organisation
UNSW Sydney
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Publisher: Authorea, Inc.
Date: 08-05-2023
DOI: 10.22541/ESSOAR.168351202.25973894/V1
Abstract: The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and litude. While improvements in the regional climate model (RCM) with bias-corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias-corrected lateral and lower boundary conditions. Results show that the RCMs with bias-corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub-daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short-term intense precipitation occurs.
Publisher: Informa UK Limited
Date: 12-03-2019
Publisher: Springer Science and Business Media LLC
Date: 13-04-2018
Publisher: Springer Science and Business Media LLC
Date: 10-03-2023
DOI: 10.1007/S00382-023-06718-6
Abstract: Improving modeling capacities requires a better understanding of both the physical relationship between the variables and climate models with a higher degree of skill than is currently achieved by Global Climate Models (GCMs). Although Regional Climate Models (RCMs) are commonly used to resolve finer scales, their application is restricted by the inherent systematic biases within the GCM datasets that can be propagated into the RCM simulation through the model input boundaries. Hence, it is advisable to remove the systematic biases in the GCM simulations prior to downscaling, forming improved input boundary conditions for the RCMs. Various mathematical approaches have been formulated to correct such biases. Most of the techniques, however, correct each variable independently leading to physical inconsistencies across the variables in dynamically linked fields. Here, we investigate bias corrections ranging from simple to more complex techniques to correct biases of RCM input boundary conditions. The results show that substantial improvements in model performance are achieved after applying bias correction to the boundaries of RCM. This work identifies that the effectiveness of increasingly sophisticated techniques is able to improve the simulated rainfall characteristics. An RCM with multivariate bias correction, which corrects temporal persistence and inter-variable relationships, better represents extreme events relative to univariate bias correction techniques, which do not account for the physical relationship between the variables.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 10-2023
Publisher: American Geophysical Union (AGU)
Date: 10-06-2021
DOI: 10.1029/2020GL092058
Abstract: Correction of atmospheric variables to remove systematic biases in global climate model (GCM) simulations before downscaling offers a means of improving climate simulation accuracy in climate change impact assessments. Various mathematical approaches have been used to correct the lateral and lower boundary conditions of regional climate models (RCMs). Most of these techniques correct only the magnitude of each variable in idually over time without regard to spatial and multivariate bias. Here, we investigate how well an RCM is able to reproduce the dependence of an observed variable based on three aspects: temporal, spatial, and multivariate. Results show that the RCM simulations with univariate bias‐corrected GCM boundary conditions perform well in capturing both temporal and spatial dependence. However, all RCM simulations do not show improvement in the representation of dependence between variables, indicating the need for alternatives that correct systematic biases in multivariate dependence in both lateral and lower boundary conditions.
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/IAHS2022-312
Abstract: & & Hydro-climatological applications often require global climate models (GCMs) outputs to assess the impacts of climate change. However, it is well known that the direct use of GCM simulations is limited as their spatial and temporal resolution are insufficient to provide output at the regional scale required in assessing changes in extreme rainfall. Although regional climate models (RCMs) forced with GCM data are widely used to resolve finer resolutions, their application is hindered by systematic biases contained in large-scale circulation patterns from driving GCM data. To deal with these considerable biases, recent studies have suggested the bias correction of the input boundary conditions of RCM.& & & & This study focuses on the impact of bias corrections in the input boundary conditions of RCM on extreme rainfall events. Three bias correction methods are used: mean, mean and variance, and nested bias correction (NBC) that corrects lag-1 autocorrelations. RCM used here is the Weather Research and Forecasting model (WRF), and the European Center for Medium-Range Weather Forecast& #8217 s (ECMWF) ERA-Interim (ERA-I) reanalysis model is used as an & #8220 observational& #8221 reference for bias correction. The downscaling is performed over the Australasian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain.& & & & Two quantitative measures are used to evaluate the impact of bias correction on the RCM output: root-mean-square errors (RMSE) and bias. Indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used to evaluate bias correction performance on extreme rainfall.& & & & It is clear from the statistics used here that bias correction on the input boundary condition produces a noticeable improvement in daily precipitation percentile indices. The results also show that the sophisticated method representing rainfall variability and long-term persistence corrects details in simulating extreme rainfall.& &
No related grants have been discovered for Youngil Kim.