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This study examines the influence of the Caspian Sea–Hindu Kush Index (CasHKI) on local and synoptic meteorology as well as on dust emissions over southwest (SW) Asia by means of National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP-NCAR) re-analysed mean sea-level pressure (MSLP), geopotential height at 700 hPa and surface meridional wind, along with meteorological data at Zabol, east Iran and Meteosat/Infrared Difference Dust Index (IDDI) retrievals. The analysis focuses on the summer period (June to September) of 2000-2014 and the winter period (November to March) of 1963-2014. The CasHKI values are mostly controlled by the MSLP anomalies over the Caspian Sea (CS) domain, varying from approximately -25 to +35 hPa in winter and from approximately-10 to +14 hPa in summer, but without a clear annual pattern. The CasHKI values are classified into four modes for each month depending on their intensity. In the summer months, the high CasHKI mode is associated with enhanced MSLP over central Asia and deepening of the Indo-Pakistan thermal low associated with the Indian summer monsoon. At 700-hPa level, the high CasHKI mode shows an enhancement of the Arabian ridge, expanding it to the north over Iran and the CS, with a concurrent strengthening of the Indian trough, leading to intensification of northerly winds along east Iran. This results in significant increase in dust activity over SWAsia, which is also apparent in the winter months. Furthermore, the intensification of the northerly flow associated with the high CasHKI modes drops the temperature and increases the relative humidity over Zabol, especially during winter. The SPRINTARS-model simulations also show increased dust emissions and concentrations for the high CasHKI values, confirming that the CasHKI variations modulate the dust activity over SW Asia throughout the year. [ABSTRACT FROM AUTHOR]

Reliability of GCM (general circulation model) simulations is a significant predictor selection criteria and seems to be essential for outcomes of the statistical downscaling. This article investigates the reliability of a wide ensemble of CMIP5 climate models in reproducing seasonal atmospheric circulation patterns over Europe and the North Atlantic, namely sea level pressure (SLP) and 500 hPa geopotential height (G500). GCMs were evaluated with respect to 1971-2000 NCEP/NCAR reanalysis data by means of MAE, correlation coefficient and standard deviation. An attempt was made to determine the range of biases introduced into the statistical models by biases in the GCM simulations. For this purpose, canonical correlation analysis (CCA) models linking large-scale atmospheric circulation and air temperature in Poland were driven by historical GCM simulations expressed as anomalies with reference to the reanalysis data. It was shown that reliability of GCMs varies considerably, both seasonally and on an inter-model basis, thus becoming unfavourable in the context of statistical downscaling application. Overall, GCM ability to reproduce G500 values is better than for SLP. The most important finding from the statistical downscaling perspective is that SLP and G500 biases may introduce considerable air temperature biases (T-GCMbiases) into the downscaled reconstructions or projections of climate change. Seasonally, the highest T-GCMbiases were found in DJF, when a significant overestimation of air temperature results from a GCM tendency to overestimate the meridional pressure gradient. A similar situation appears to be a significant factor in MAM and SON. Multiyear average GCM biases do not, however, explain a significant part of the inter-model variability of T-GCMbias, thus highlighting the need for a more in-depth evaluation. Nevertheless, good GCM performance seems to ensure that T-GCMbias is not of a considerable value. Overall, the results of the article emphasise the necessity for undertaking an application-oriented assessment of GCM reliability prior to any statistical downscaling approach. [ABSTRACT FROM AUTHOR]