Abstract:
Considering the importance of simulation of climate system in this present day, it is very
important to correct some biases, since it has been established that biases exist from simulated
data gotten from climate models. Due to reasons such as parameterization, initial value
problem, etc, Regional Climate Models driven by Global Climate Models will always contain
some biases compared to observational data. Therefore, this study attempt to bias correct
precipitation and temperature of simulated data to be close to observational data of some
stations. This study made use of five stations to carryout temporal analysis; namely: Calabar,
Gusau, Ibi, Ikeja, and Maiduguri while five additional stations were added to carryout spatial
analysis for more accuracy; namely: Bauchi, Minna, Ilorin, Lokoja and Asabar.
Two packages under quantile mapping were chosen to execute this bias correction and the two
packages representing the methods are Quantile-Quantile (QQ) method and Parametric
Transformation (PTF) Method. The study used daily data of the stations mentioned earlier for
observed and RegCM3 data driven by two GCMs namely; Met Office Hadley Centre
(MOHC) and Max Plank Instsitute (MPI) was used for simulated data, The data were splitted
into historical and future, the historical data runs from 1975 – 2000, while the future data runs
from 2010 – 2030 Representative Concentration Pathway (RCP 4.5 Scenario). R software was
used to run the both methods of bias correction while excel and ArcGIS was used to analyse
temporally and spatially respectively.
Coefficient of Effieciency helped to identify which of the two methods best bias correct the
simulated data using taylor’s diagram, which evidently was seen to be Parametric
Transformation (PTF) method and also it was observed that the amongst the two GCMs,
MOHC allow for better performance of bias correction. Therefore, Parametric
Transformation (PTF) method was then used for future impact scenario on RegCM3 driven
by MOHC. Fits generated when historical bias correction was carried out was then used fit the
future impact scenario, where after the analysis it was compared to raw simulated data used
for future impact scenario and series of overestimation and underestimation was observed
which would have affected decision making if not corrected.
