Abstract
The evaluation of general circulation models (GCM) is a fundamental step in climate research in terms of both quality assurance/quality control and realistic representation of the dynamics of the atmospheric flows in the future projections. In this paper, a statistical method is introduced to evaluate GCMs with respect to teleconnection patterns in the winter 500 hPa geopotential height field over the Northern Hemisphere (NH). The procedure uses the combination of negative extrema method and receiver operating characteristic (ROC) curve analysis. The proposed method is demonstrated using selected general circulation models (GCMs) disseminated by the CMIP5 project. The ERA-20C reanalysis was used as a reference, supported by the NCEP/NCAR R1 reanalysis. The proposed method enables us to track changes in the geographical positions of the action centers (ACs); therefore, to detect improvement/deterioration in the GCM performance with time. It was found that the majority of the GCMs reproduce prominent teleconnections of the NH but fail to capture the eastward shift of the ACs over the Pacific Ocean in the last decades of the 20th century. The GCMs reproduce teleconnections with stronger correlations over the north-western part of the Atlantic Ocean compared to the reanalyses. The construction of mobile teleconnection indices is proposed to gain further insight into the performance of the models and to support a regional-scale analysis. The method can be easily applied to the recent CMIP6 simulations.
Highlights
In the 21st century, one of the most essential tasks of climate research is to assess the extent and severity of climate change in a spatially explicit manner for the forthcoming generations
As teleconnections are quasi-stationary standing waves, the CP maps obtained from the reanalyses with the most frequently observable clusters are considered to be the imprint of the hemispherical teleconnection patterns
The area under the ROC curve (AUC) values associated with each time period are summarized in Figure 4
Summary
In the 21st century, one of the most essential tasks of climate research is to assess the extent and severity of climate change in a spatially explicit manner for the forthcoming generations. After the adoption of the Paris Agreement [1], numerous studies aimed to provide an accurate estimation of the expected effects of global warming based on the future scenarios of general circulation models (GCMs) [2,3,4,5,6]. Global warming may lead to more frequent extreme heatwave events [3], severe droughts (e.g., in the Mediterranean region) [4,8], and extreme precipitation events [9]. Global climate change is associated with diverse socioeconomic effects [11]
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