General circulation models for rainfall simulations: Performance assessment using complex networks

Abstract

Reliable assessment of the impact of climate change on hydrology in a region requires proper selection of General Circulation Models (GCMs). The present study introduces the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall and selecting an ensemble of the best-performing GCMs. The performance of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating monthly rainfall over India is assessed. The observed (interpolated and gridded rainfall provided by the India Meteorological Department) rainfall data and GCM-simulated rainfall data over the period 1961–2014 at a spatial resolution of 1° x 1° (a total of 288 grids) are studied. The GCMs are evaluated for two cases: (1) Case 1 – whole year rainfall; and (2) Case 2 – summer monsoon rainfall (June–September). The clustering coefficient of the rainfall network is used as a network measure to evaluate the ability of the GCMs in simulating rainfall. For rainfall network construction, each grid is considered as a network. The phase space reconstruction concept is used to reconstruct the single-variable rainfall time series in a multi-dimensional phase space to represent the rainfall dynamics. The optimal dimension for reconstruction is determined using the false nearest neighbor (FNN) algorithm. For network construction, each reconstructed vector is considered as a node, and the connections between them, identified using a distance threshold for the reconstructed vectors in the phase space, serve as the links. For each of the 288 grids, the GCMs are ranked based on the difference in the clustering coefficient between the observed and GCM-simulated rainfall networks. The group decision-making (GDM) approach is employed to identify the ensemble of the best-performing GCMs for the entire study area considering all the grids. The results suggest different models perform well for the whole-year rainfall and summer monsoon rainfall. For the whole-year rainfall, the models CMCC-CM2-HR4, GFDL-ESM4, CMCC-ESM2, EC-Earth3-AerChem, and CMCC-CM2-SR5 rank as the top five. For the summer monsoon rainfall, FIO-ESM-2-0, E3SM-1-1, CESM2-FV2, CMCC-CM2-HR4, and CMCC-CM2-SR5 perform the best. Considering the rank of each model for both the cases, the best-performing models are identified to be CMCC-CM2-HR4, CMCC-CM2-SR5, CMCC-ESM2, FIOESM-2-0, and E3SM-1-1. The rainfall simulated from these models also show close resemblance to the observed rainfall, especially in terms of monthly mean rainfall. Therefore, even among the many GCMs that show good agreement with the monthly mean observed rainfall, the clustering coefficient-based analysis helps to narrow down the models for the ensemble of best-performing GCMs.