Abstract
The use of global and regional climate models has been increasing in the past few decades, in order to analyze the future of natural resources and the socio-economic aspects of climate change. However, these climate model outputs can be quite biased, which makes it challenging to use them directly for analysis purpose. Therefore, a tool named Climate Data Bias Corrector was developed to correct the bias in climatic projections of historical and future periods for three primary climatic variables—rainfall, temperature (maximum and minimum), and solar radiation. It uses the quantile mapping approach, known for its efficiency and low computational cost for bias correction. Its Graphical User Interface (GUI) was made to be feasible to take input and give output in commonly used file formats—comma and tab delimited file formats. It also generates month-wise cumulative density function (CDF) plot of a random station/grid to allow the user to investigate the effectiveness of correction statistically. The tool was verified with a case study on several agro-ecological zones of India and found to be efficient.
Highlights
The changing climate is increasingly seizing the attention of scientist communities, irrespective of their fields of interest, in the last few decades
30-year time series rainfall, temperature, and solar radiation weredaily compared with of themulti-ensemble observed timeGCMs series derived of the 1990s, at a
The Climate Data Bias Corrector (CDBC) tool was used to remove the bias from all five global climate models (GCMs) data for historical
Summary
The changing climate is increasingly seizing the attention of scientist communities, irrespective of their fields of interest, in the last few decades. Day-by-day, studies on climate change, and its impact on different ecological systems are becoming familiar with the increasing use of Coupled. Model Inter-comparison Project (CMIP) derived future projections of global climate models (GCMs). With advancing technology and computational methods, CMIP has been continuously working on further improvement of GCM simulated outputs [1,2,3]. There are many research articles, which have explicitly mentioned that the direct use of GCM-simulated projections is still unrealistic, due to humongous uncertainty and bias present in them [4,5,6]. Different downscaling methods have been developed to remove the bias and uncertainty from the GCM outputs. They are categorized into two major types—dynamic and statistical down-scaling
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