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Climate bias correction

From EverybodyWiki Bios & Wiki

Future climate scenarios simulated by Regional Climate Models (RCMs) are often used as input to hydrological models to study the effects of a changing climate on our water resources. Yet, RCM models face crucial biases such as systematic wet or cold biases during winter caused by an inadequate parameterization or by an inaccurate representation of physical processes in the RCMs[1]. In order to overcome these biases, different statistical methods have been suggested in the scientific literature that employ a transformation algorithm to re-scale RCM outputs. Some of these methods (e.g. univariate methods that adjust only one RCM-simulated variable at a time) are comparatively easy to implement, while others (e.g., multivariate correction that guarantees consistency in spatiotemporal fields and different climate variables) are more complex and require advanced statistical knowledge and more computing power[2]. The adequacy of each bias correction method varies depending on several factors such as the studied watershed, the applied RCM model, or the utilized climate variable[3].


References

  1. Cannon, Alex J. (2016-05-06). "Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure". Journal of Climate. 29 (19): 7045–7064. doi:10.1175/JCLI-D-15-0679.1. ISSN 0894-8755.
  2. Teutschbein, Claudia; Seibert, Jan (August 2012). "Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods". Journal of Hydrology. 456-457: 12–29. doi:10.1016/j.jhydrol.2012.05.052. ISSN 0022-1694.
  3. García, Jesús E.; Fernández, M. (2013). "Copula based model correction for bivariate Bernoulli financial series". AIP. doi:10.1063/1.4825801.


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