Introducing Quantile Mapping to a Regression Model Using a Multi-Model Ensemble to Improve Probabilistic Projections of Monthly Precipitation
Noriko N. Ishizaki*, Koji Dairaku*,, and Genta Ueno**
*National Research Institute for Earth Science and Disaster Resilience
3-1 Tennodai, Tsukuba-city, Ibaraki 305-0006, Japan
**The Institute of Statistical Mathematics, Tokyo, Japan
A new method was proposed for the probabilistic projection of future climate that introduced quantile mapping to a regression method using a multi-model ensemble (QM_RMME). Results of this method were then compared with those of the traditional regression method (RMME). Six stations in Japan where 100 year observation records were available were used to evaluate the performance of the methods. An initial 50-year period (1901–1950) was used to develop the regression models and the final period (1951–2000) was used for evaluation. Results showed that the estimation errors at the 50th and 90th percentile were smaller for QM_RMME as compared to RMME at most sites. Conversely, when the model development and evaluation periods were limited to 20 years (1901–1920 and 1951–1970, respectively), the 90th percentile error was larger for QM_RMME. This was attributed to quantile mapping resulting in over-fitting of the data during the model development period. Furthermore, the QM_RMME error increased when the difference of observations between the model development and verification periods was large. Therefore, results indicated that the RMME method was more stable for relatively short data verification periods.
-  G. R. Harris, M. Collins, D. H. M. Sexton, J. M. Murphy, and B. B. B. Booth, “Probabilistic projections for 21st century European climate,” Natural Hazards and Earth System Sciences, Vol.10, No.9, pp. 2009-2020, doi:10.5194/nhess-10-2009-2010, 2010.
-  A. B. Pittock, R. N. Jones, and C. D. Mitchell, “Probabilities will help us plan for climate change,” Nature, Vol.413, No.6853, p. 249, doi:10.1038/35095194, 2001.
-  M. New, A. Lopez, S. Dessai, and R. Wilby, “Challenges in using probabilistic climate change information for impact assessments: an example from the water sector,” Philosophical Transactions of the Royal Society A, Vol.365, No.1857, pp. 2117-2131, doi:10.1098/rsta.2007.2080, 2007.
-  B. V. Christensen, J. P. Vidal, and S. D. Wade, “Using UKCP09 probabilistic climate information for UK water resource planning,” J. of Hydrology, Vol.424-425, pp. 48-67, doi:10.1016/j.jhydrol.2011.12.020, 2012.
-  T. J. Coulthard, J. Ramirez, H. J. Fowler, and V. Glenis, “Using the UKCP09 probabilistic scenarios to model the amplified impact of climate change on drainage basin sediment yield,” Hydrology and Earth System Sciences, Vol.16, No.11, pp. 4401-4416, doi:10.5194/hess-16-4401-2012, 2012.
-  W. Tian and P. Wilde, “Thermal building simulation using the UKCP09 probabilistic climate projections,” J. of Building Performance Simulation, Vol.4, No.2, pp. 105-124, doi:10.1080/19401493.2010.502246, 2010.
-  N. N. Ishizaki, K. Dairaku, and G. Ueno, “Regional probabilistic climate projection for Japan with a regression model using multi-model ensemble experiments,” Hydrological Research Letters, Vol.11, No.1, pp. 44-50, doi:10.3178/hrl.11.44, 2017.
-  H. Li, J. Sheffield, and E. F. Wood, “Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching,” J. of Geophysical Research, Vol.115, No.D10, doi:10.1029/2009JD012882, 2010.
-  D. W. Pierce, D. R. Cayan, E. P. Maurer, J. T. Abatzoglou, and K. C. Hegewisch, “Improved bias correction techniques for hydrological simulations of climate change,” J. of Hydrometeorology, Vol.16, No.6, pp. 2421-2442, doi:10.1175/JHM-D-14-0236.1, 2015.
-  S. Watanabe, S. Kanae, S. Seto, P. J.-F. Yeh, Y. Hirabayashi, and T. Oki, “Intercomparison of bias-correction methods for monthly temperature and precipitation simulated by multiple climate models,” J. of Geophysical Research, Vol.117, No.D23, doi:10.1029/2012JD018192, 2012.
-  H. Zou and T. Hastie, “Regularization and variable selection via the elastic net,” J. of the Royal Statistical Society, Series B, Vol.67, No.2, pp. 301-320, doi:10.1111/j.1467-9868.2005.00503.x, 2005.
-  I.-K. Yeo and R. A. Johnson, “A new family of power transformations to improve normality or symmetry,” Biometrika, Vol.87, No.4, pp. 954-959, doi:10.1093/biomet/87.4.954, 2000.
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