Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia

<p>Reanalysis datasets are critical in climate research and weather analysis, offering consistent historical weather and climate data crucial for understanding atmospheric phenomena and validating climate models. However, biases exist in reanalysis datasets that would affect their applications...

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Main Authors: K. K. W. Cheung, F. Ji, N. Nishant, J. Teng, J. Bennett, D. L. Liu
Format: Article
Language:English
Published: Copernicus Publications 2025-08-01
Series:Hydrology and Earth System Sciences
Online Access:https://hess.copernicus.org/articles/29/3527/2025/hess-29-3527-2025.pdf
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author K. K. W. Cheung
F. Ji
F. Ji
N. Nishant
J. Teng
J. Bennett
D. L. Liu
author_facet K. K. W. Cheung
F. Ji
F. Ji
N. Nishant
J. Teng
J. Bennett
D. L. Liu
author_sort K. K. W. Cheung
collection DOAJ
description <p>Reanalysis datasets are critical in climate research and weather analysis, offering consistent historical weather and climate data crucial for understanding atmospheric phenomena and validating climate models. However, biases exist in reanalysis datasets that would affect their applications under circumstances. This study evaluates BARRA, which is a high-resolution reanalysis for the Australian region, and ERA5 in simulating mean precipitation and six selected precipitation extremes for their climatology, temporal correlation, coefficient of variation, and trend. Both datasets reproduce daily timescale probability density distributions and spatial patterns of mean precipitation well with minor biases. ERA5 shows stronger temporal correlations, superior inter-annual precipitation accuracy, and lower biases in coefficient of variation compared to BARRA, especially in Northern Australia. However, both models exhibit substantial biases in trend, underestimating increasing trends in Northern Australia. ERA5 underestimates dry days and heavy rainfall, while BARRA tends to overestimate these extremes. Temporal correlations for extreme precipitation indices are weaker compared to mean annual precipitation. Notable differences exist in variability biases, with BARRA showing larger biases, especially for heavy precipitation in inland regions and Northern Australia. While both datasets replicate the main trends, biases persist. Overall, the evaluation results support application of both datasets for climatology analyses, but caution is advised for variability and trend analyses, particularly for specific extremes.</p>
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spelling doaj-art-2dfea49b8d6b45b9b2e589d9880665372025-08-20T03:02:36ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382025-08-01293527354310.5194/hess-29-3527-2025Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over AustraliaK. K. W. Cheung0F. Ji1F. Ji2N. Nishant3J. Teng4J. Bennett5D. L. Liu6School of Emergency Management, Nanjing University of Information Science and Technology, Jiangsu 210044, ChinaScience and Insights Division, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, NSW 2150, AustraliaAustralian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, AustraliaClimate Change Research Centre, University of New South Wales, Sydney, NSW, AustraliaCSIRO Environment, GPO Box 1700, Canberra, ACT 2601, AustraliaCSIRO Environment, Research Way, Clayton, VIC 3168, AustraliaNSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia<p>Reanalysis datasets are critical in climate research and weather analysis, offering consistent historical weather and climate data crucial for understanding atmospheric phenomena and validating climate models. However, biases exist in reanalysis datasets that would affect their applications under circumstances. This study evaluates BARRA, which is a high-resolution reanalysis for the Australian region, and ERA5 in simulating mean precipitation and six selected precipitation extremes for their climatology, temporal correlation, coefficient of variation, and trend. Both datasets reproduce daily timescale probability density distributions and spatial patterns of mean precipitation well with minor biases. ERA5 shows stronger temporal correlations, superior inter-annual precipitation accuracy, and lower biases in coefficient of variation compared to BARRA, especially in Northern Australia. However, both models exhibit substantial biases in trend, underestimating increasing trends in Northern Australia. ERA5 underestimates dry days and heavy rainfall, while BARRA tends to overestimate these extremes. Temporal correlations for extreme precipitation indices are weaker compared to mean annual precipitation. Notable differences exist in variability biases, with BARRA showing larger biases, especially for heavy precipitation in inland regions and Northern Australia. While both datasets replicate the main trends, biases persist. Overall, the evaluation results support application of both datasets for climatology analyses, but caution is advised for variability and trend analyses, particularly for specific extremes.</p>https://hess.copernicus.org/articles/29/3527/2025/hess-29-3527-2025.pdf
spellingShingle K. K. W. Cheung
F. Ji
F. Ji
N. Nishant
J. Teng
J. Bennett
D. L. Liu
Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia
Hydrology and Earth System Sciences
title Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia
title_full Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia
title_fullStr Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia
title_full_unstemmed Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia
title_short Comparison of BARRA and ERA5 in replicating mean and extreme precipitation over Australia
title_sort comparison of barra and era5 in replicating mean and extreme precipitation over australia
url https://hess.copernicus.org/articles/29/3527/2025/hess-29-3527-2025.pdf
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