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Bias Correction of Historical and Future Simulations of Precipitation and Temperature for China from CMIP5 Models
- Source :
- Journal of Hydrometeorology. 19:609-623
- Publication Year :
- 2018
- Publisher :
- American Meteorological Society, 2018.
-
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.
- Subjects :
- Atmospheric Science
010504 meteorology & atmospheric sciences
Cumulative distribution function
0208 environmental biotechnology
Climate change
02 engineering and technology
01 natural sciences
020801 environmental engineering
Climatology
Environmental science
Bias correction
Equidistant
Climate model
Precipitation
0105 earth and related environmental sciences
Quantile
Subjects
Details
- ISSN :
- 15257541 and 1525755X
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- Journal of Hydrometeorology
- Accession number :
- edsair.doi.dedup.....9e9b0c3139c530a0fbbbe28a87fe7a64