In:
Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 23, No. 11 ( 2019-11-25), p. 4803-4824
Abstract:
Abstract. The climate modelling community has trialled a large
number of metrics for evaluating the temporal performance of general circulation
models (GCMs), while very little attention has been given to the assessment
of their spatial performance, which is equally important. This study
evaluated the performance of 36 Coupled Model Intercomparison Project 5
(CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon,
winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum
temperature over Pakistan using state-of-the-art spatial metrics, SPAtial
EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V,
Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The
multi-model ensemble (MME) precipitation and maximum and minimum temperature
data were generated through the intelligent merging of simulated
precipitation and maximum and minimum temperature of selected GCMs employing
random forest (RF) regression and simple mean (SM) techniques. The results indicated
some differences in the ranks of GCMs for different spatial metrics. The
overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the
best GCMs in simulating the spatial patterns of mean annual, monsoon,
winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum
temperature over Pakistan. MME precipitation and maximum and minimum
temperature generated based on the best-performing GCMs showed more
similarities with observed precipitation and maximum and minimum temperature
compared to precipitation and maximum and minimum temperature simulated by
individual GCMs. The MMEs developed using RF displayed better performance
than the MMEs based on SM. Multiple spatial metrics have been used for the
first time for selecting GCMs based on their capability to mimic the spatial
patterns of annual and seasonal precipitation and maximum and minimum
temperature. The approach proposed in the present study can be extended to
any number of GCMs and climate variables and applicable to any region for
the suitable selection of an ensemble of GCMs to reduce uncertainties in
climate projections.
Type of Medium:
Online Resource
ISSN:
1607-7938
DOI:
10.5194/hess-23-4803-2019
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2019
detail.hit.zdb_id:
2100610-6
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