A Procedure to Reduce the Uncertainty in Regional-Scale Climate Change
A large number of General Circulation Models (GCMs) are currently
available for modelling the atmospheric conditions over the Earth.
However, there is a large variability in the future climate predicted by
the available set of climate models. Hence, the climate data introduces
the most amount of uncertainty in the climate change impact assessment.
Regional-scale climate change impact studies based on these models may
produce a wide range of possible impacts that becomes unusable for
policymakers. A robust GCM selection procedure is introduced in the
current study to bring the uncertainty to a realistic range. The
proposed approach takes into account the process representation in the
climate models by checking teleconnections in data along with their
ability to predict the regional climate in spatial and temporal scale.
The interdependence between the climate models are also accounted for in
the proposed approach to avoid underestimation of uncertainty. The
procedure is validated in the Bharathapuzha River Basin, Kerala, India.
The study considers 22 GCMs that participated in the Coupled Model
Inter-comparison Project-5 and 6 Regional Climate Models (RCMs) that are
recommended for the Indian subcontinent. The climate models BNU-ESM,
CMCC-CM, GFDL-ESM2G, GFDL-ESM2M and MPI-ESM-MR are found to be
performing well for the prediction of both precipitation and
temperature. The proposed climate model selection procedure can bring
down the band width of uncertainty from 376 mm to 162 mm in monthly
rainfall prediction with a containing ratio of 44%. The downscaling of
the climate predictions can further increase the containing ratio by
removing the systematic error. The bandwidth of uncertainty has reduced
from 10.82 K to 3.83 K in the prediction of minimum temperature and from
8.35 K to 4.52 K for maximum temperature. The proposed GCM selection
procedure provides more confidence in the predicted future climate since
regionally significant correlations between climate variables are
preserved in the selected models. The model selection procedure is
validated for the period 2006-2018 with the observed climatic variables,
and the selected models are found to be performing well.