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Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models

Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models
Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models

In this study, the equidistant cumulative distribution function (EDCDF) quantile-based mapping method was used to develop bias-corrected and downscaled monthly precipitation and temperature for China at 0.5° × 0.5° spatial resolution for the period 1961-2099 for eight CMIP5 GCM simulations. The downscaled dataset was constructed by combining observations from 756 meteorological stations across China with the monthly GCM outputs for the historical (1961-2005) and future (2006-99) periods for the lower (RCP2.6), medium (RCP4.5), and high (RCP8.5) representative concentration pathway emission scenarios. The jackknife method was used to cross validate the performance of the EDCDF method and was compared with the traditional quantile-based matching method (CDF method). This indicated that the performance of the two methods was generally comparable over the historic period, but the EDCDF was more efficient at reducing biases than the CDF method across China. The two methods had similar mean absolute error (MAE) for temperature in January and July. The EDCDF method had a slight advantage over the CDF method for precipitation, reducing the MAE by about 0.83% and 1.2% at a significance level of 95% in January and July, respectively. For future projections, both methods exhibited similar spatial patterns for longer periods (2061-90) under the RCP8.5 scenario. However, the EDCDF was more sensitive to a reduction in variability.

Climate change, Climate models, Climate prediction
1525-755X
609-623
Yang, X.
c9b6c6a8-77b0-4c7e-b681-9b11d73e392d
Wood, E.F.
0929fcf4-b795-4e99-9f98-371932bef995
Sheffield, J.
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Ren, L.
f7af5cd8-fa28-456c-9255-517de687dd72
Zhang, M.
05e76651-c3a5-48d6-a963-f6e66a7c80f6
Wang, Y.
6e2e14f6-6aee-46e4-81ab-fd842e00cf91
Yang, X.
c9b6c6a8-77b0-4c7e-b681-9b11d73e392d
Wood, E.F.
0929fcf4-b795-4e99-9f98-371932bef995
Sheffield, J.
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Ren, L.
f7af5cd8-fa28-456c-9255-517de687dd72
Zhang, M.
05e76651-c3a5-48d6-a963-f6e66a7c80f6
Wang, Y.
6e2e14f6-6aee-46e4-81ab-fd842e00cf91

Yang, X., Wood, E.F., Sheffield, J., Ren, L., Zhang, M. and Wang, Y. (2018) Bias correction of historical and future simulations of precipitation and temperature for China from CMIP5 models. Journal of Hydrometeorology, 19 (3), 609-623. (doi:10.1175/JHM-D-17-0180.1).

Record type: Article

Abstract

In this study, the equidistant cumulative distribution function (EDCDF) quantile-based mapping method was used to develop bias-corrected and downscaled monthly precipitation and temperature for China at 0.5° × 0.5° spatial resolution for the period 1961-2099 for eight CMIP5 GCM simulations. The downscaled dataset was constructed by combining observations from 756 meteorological stations across China with the monthly GCM outputs for the historical (1961-2005) and future (2006-99) periods for the lower (RCP2.6), medium (RCP4.5), and high (RCP8.5) representative concentration pathway emission scenarios. The jackknife method was used to cross validate the performance of the EDCDF method and was compared with the traditional quantile-based matching method (CDF method). This indicated that the performance of the two methods was generally comparable over the historic period, but the EDCDF was more efficient at reducing biases than the CDF method across China. The two methods had similar mean absolute error (MAE) for temperature in January and July. The EDCDF method had a slight advantage over the CDF method for precipitation, reducing the MAE by about 0.83% and 1.2% at a significance level of 95% in January and July, respectively. For future projections, both methods exhibited similar spatial patterns for longer periods (2061-90) under the RCP8.5 scenario. However, the EDCDF was more sensitive to a reduction in variability.

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2018117Revision_JHM-D-17-0180 - Accepted Manuscript
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More information

Accepted/In Press date: 1 March 2018
e-pub ahead of print date: 3 April 2018
Keywords: Climate change, Climate models, Climate prediction

Identifiers

Local EPrints ID: 420495
URI: http://eprints.soton.ac.uk/id/eprint/420495
ISSN: 1525-755X
PURE UUID: 174248a1-e9bf-4909-90d0-ce4adac11a6d
ORCID for J. Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 09 May 2018 16:30
Last modified: 09 Nov 2022 02:48

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Contributors

Author: X. Yang
Author: E.F. Wood
Author: J. Sheffield ORCID iD
Author: L. Ren
Author: M. Zhang
Author: Y. Wang

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