Evaluation of statistical corrective methods to minimize bias at different time scales in a regional climate model driven data. imate model driven data.

Abstract

The regional climate models provide sufficient information of the climate data, which can be used for simulating the impact of expected climate change on crop growth and hydrological processes. But future climate data derived from such models often suffers from bias and is not ready to use per se in crop growth/hydrological models, wherein reasonable and consistent meteorological daily input data is a crucial factor. The present study concerns the assessment and minimization of the bias in the PRECIS modeled data of maximum and minimum temperatures and rainfall for Ludhiana station, representing central Punjab of India. The correction functions for three corrective methods i.e. difference, modified difference and statistical bias correction at daily, monthly and annual time scales were developed and validated to minimize the bias. Amongst these, correction functions derived using modified difference method at daily time scale for rainfall and at monthly time scale for Tmax and Tmin were found to be the superseding.