首页|Adaptive ensemble weighting for GCMs to enhance future drought characterization under various climate change scenarios
Adaptive ensemble weighting for GCMs to enhance future drought characterization under various climate change scenarios
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NETL
NSTL
Springer Nature
Abstract Drought is one the most complex and catastrophic natural hazards. Global Climate Models (GCMs) have become increasingly popular for predicting future drought characteristics. However, errors and interdependence between the time series data of GCMs reduce the accuracy of drought characterization. This article introduces a new weighting scheme for multi-model ensemble to integrate the features of multiple GCM-based simulations of precipitation, called the Minimum Redundancy Maximum Relevance Adaptive Weighting Scheme (MRMRAWS). To evaluate the performance of MRMRAWS, this study uses simulations from 18 different GCMs, which represent monthly precipitation data from 1950 to 2014. Data were collected from 1817 uniformly distributed grid points on the Tibetan Plateau (TP). In addition, simulations from three future scenarios, covering monthly precipitation records from 2016 to 2100, are used to assess the temporal behavior of drought and its various classes. The results of this research have two main facets. First, the proposed MRMRAWS outperforms other widely used ensemble weighting schemes, advocating its use to improve the ensemble of GCMs. Second, the evaluation of future drought characteristics indicates that recurrent droughts are likely to persist in the TP region under longer time scales and higher-emission scenarios.
Muhammad Shakeel、Hussnain Abbas、Ayesha Waseem、Zulfiqar Ali
NETL
NSTL
Springer Nature
