Statistical downscaling of general circulation model outputs to precipitation-part 2 : Bias-correction and future projections
- Sachindra, Dhanapala, Huang, Fuchun, Barton, Andrew, Perera, Bimalka
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3282-3303
- Full Text:
- Reviewed:
- Description: This article is the second of a series of two articles. In the first article, two models were developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and HadCM3 outputs, for statistically downscaling these outputs to monthly precipitation at a site in north-western Victoria, Australia. In that study, it was seen that the downscaling model developed with NCEP/NCAR reanalysis outputs performs much better than the model developed with HadCM3 outputs. Furthermore, it was found that there is large bias in HadCM3 outputs which needs to be corrected. In this article, the downscaling model developed with NCEP/NCAR reanalysis outputs was used to downscale HadCM3 20th century climate experiment outputs to monthly precipitation over the period 1950-1999. In all four seasons, the precipitation downscaled with HadCM3 20th century outputs, displayed a large scatter and the majority of precipitation was overestimated. The precipitation downscaled with HadCM3 outputs was bias-corrected against the observed precipitation pertaining to the period 1950-1999, using three techniques: (1) equidistant quantile mapping (EDQM), (2) monthly bias-correction (MBC) and (3) nested bias-correction (NBC). Although all these bias-correction techniques were able to adequately correct the statistics of downscaled precipitation, the magnitude of the scatter of precipitation remained almost the same. Considering the performances and its ability to correct the cumulative distribution of precipitation, EDQM was selected for the bias-correction of future precipitation projections. HadCM3 outputs for the A2 and B1 greenhouse gas scenarios were introduced to the downscaling model and the downscaled precipitation for the period 2000-2099 was bias-corrected with the EDQM technique. Both A2 and B1 scenarios indicated a rise in the average of future precipitation in winter and a drop in it in summer and spring. These scenarios showed an increase in the maximum monthly precipitation in all seasons and an increase in percentage of months with zero precipitation in summer, autumn and spring. © 2014 Royal Meteorological Society
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3282-3303
- Full Text:
- Reviewed:
- Description: This article is the second of a series of two articles. In the first article, two models were developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and HadCM3 outputs, for statistically downscaling these outputs to monthly precipitation at a site in north-western Victoria, Australia. In that study, it was seen that the downscaling model developed with NCEP/NCAR reanalysis outputs performs much better than the model developed with HadCM3 outputs. Furthermore, it was found that there is large bias in HadCM3 outputs which needs to be corrected. In this article, the downscaling model developed with NCEP/NCAR reanalysis outputs was used to downscale HadCM3 20th century climate experiment outputs to monthly precipitation over the period 1950-1999. In all four seasons, the precipitation downscaled with HadCM3 20th century outputs, displayed a large scatter and the majority of precipitation was overestimated. The precipitation downscaled with HadCM3 outputs was bias-corrected against the observed precipitation pertaining to the period 1950-1999, using three techniques: (1) equidistant quantile mapping (EDQM), (2) monthly bias-correction (MBC) and (3) nested bias-correction (NBC). Although all these bias-correction techniques were able to adequately correct the statistics of downscaled precipitation, the magnitude of the scatter of precipitation remained almost the same. Considering the performances and its ability to correct the cumulative distribution of precipitation, EDQM was selected for the bias-correction of future precipitation projections. HadCM3 outputs for the A2 and B1 greenhouse gas scenarios were introduced to the downscaling model and the downscaled precipitation for the period 2000-2099 was bias-corrected with the EDQM technique. Both A2 and B1 scenarios indicated a rise in the average of future precipitation in winter and a drop in it in summer and spring. These scenarios showed an increase in the maximum monthly precipitation in all seasons and an increase in percentage of months with zero precipitation in summer, autumn and spring. © 2014 Royal Meteorological Society
Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation
- Sachindra, Dhanapala, Huang, Fuchun, Barton, Andrew, Perera, Bimalka
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3264-3281
- Full Text:
- Reviewed:
- Description: This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article.
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3264-3281
- Full Text:
- Reviewed:
- Description: This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article.
- «
- ‹
- 1
- ›
- »