A software toolkit for correcting systematic biases in climate model simulations. (June 2018)

• Record Type: Journal Article
• Authors: Mehrotra, R.; Johnson, F.; Sharma, A.
• Is Part Of: Environmental modelling & software. Volume 104(2018)
• Page Start: 130
• Page End: 152
• Publication Date: 2018-06
• Subjects: Bias correction -- R package -- Persistence -- Multivariate -- Distribution based quantile matching -- Nested bias correction; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.02.010
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Simulations from climate models require bias correction prior to use in impact assessments or for statistical or dynamic downscaling to finer scales. There are a number of different approaches to bias correction, although most of these focus on a single variable for a particular location. Another limitation is that often corrections are only applied for one time scale of interest, for example daily or monthly aggregated simulations despite evidence of different bias structures existing at different time scales. Recent works have sought to address each of these limitations and have led to the development of the Multivariate Recursive Nesting Bias Correction (MRNBC) and Multivariate Recursive Quantile-matching Nested Bias Correction (MRQNBC) methods. An open-source software toolkit in the R statistical computing environment has been developed to provide access to these methods. Several applications of the software are demonstrated in this paper along with information about the capabilities of the software. Highlights: An approach to apply multivariate, mutli-timscale bias correction approach. Available as an open-source software package. Includes statistical analysis and graphics functions at multiple time scales. Preserves observed across-variables dependence in the bias corrected time series. Preserves distributional attributes at multiple time scales.