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Assessment of GCM capabilities to simulate tropospheric stability on the Arabian Peninsula
Author(s) -
Barfus Klemens,
Bernhofer Christian
Publication year - 2014
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4092
Subject(s) - climatology , environmental science , precipitation , atmospheric research , atmospheric instability , stability (learning theory) , troposphere , meteorology , index (typography) , climate model , atmospheric sciences , climate change , geography , geology , computer science , world wide web , wind speed , oceanography , machine learning
Stability indices provide simple metrics to characterize tropospheric states favouring convection. Not only for regions where precipitation is mainly related to convective processes, important aspects of the climate comprising information from different levels and variables can therefore be summarized in scalar metrics. Although the linkage between tropospheric stability and convective precipitation is blurred by additional prerequisites often not resolved in atmospheric models, analysis of stability indices from global climate models (GCMs) provides a more process‐orientated assessment than the separate analysis of the individual climate variables. This paper presents an assessment of GCM capabilities to simulate tropospheric stability on the Arabian Peninsula. Therefore, six stability indices (K‐Index, Total Totals Index, Vertical Totals Index, Showalter Index, SWEAT and Cross Totals Index) were calculated for several GCMs from the Climate Model Intercomparison Project 3 archive for three locations. GCM indices were compared with reanalysis data from the National Centers for Environmental Prediction in collaboration with the National Center for Atmospheric Research as well as radiosoundings from the Integrated Global Radiosounding Archive. Comparison was done by means of a classification approach based on quantile values of the index distribution. Input parameters of the indices were also analysed and biases for indices as well as input parameters were identified. There are biases found for the different indices which can be attributed to some extent to biases of the input parameters. There is no input parameter which is biased in the same direction for all indices and locations. Due to the variability of the results, rankings of the models are characterized by large rank differences so that there is no overall best performing model. Nevertheless, this study provides some insight in the performance of the GCMs to simulate tropospheric stability at individual locations and so presents an alternative method of model performance assessment.