Middle East climate simulation and dominant precipitation processes

Abstract

The ability of both the ECMWF-TOGA analyses and regional model RegCM2 to simulate the climate of the Middle East is examined. The climate of the region displays high spatial, seasonal and interannual variability, providing a strong test of a climate model's abilities. The higher resolution of RegCM2, compared with the ECMWF analyses, allows it to capture the spatial variability of temperature and precipitation better despite model biases being present. Both RegCM2 and the analyses have a cold bias, exacerbated in RegCM2 by a bias present in the prescribed sea-surface-temperature forcing. RegCM2 does not capture the annual cycle of precipitation on the Black and Caspian Sea coasts, where very steep topography exists, nor on the eastern Mediterranean coast, where the coastal mountains are not resolved. RegCM2 does capture the seasonal cycle in the Fertile Crescent and Zagros Mountains, where it is strongly influenced by a plateau circulation above the Iranian plateau. It is shown that accurate simulation of precipitation in these regions requires the correct simulation of storm tracks, topographic interactions and atmospheric stability. RegCM2 is better able to simulate the interannual variability averaged over the entire domain compared with the ECMWF analyses; however, they both have difficulty reproducing the interannual variability in particular subregions. It is also shown that processes controlling the seasonality of precipitation differ in different subregions and are often different from the processes controlling interannual variability. This suggests that, in order to model precipitation successfully, a horizontal scale that allows differentiation of precipitation zones dominated by different precipitation processes, both seasonally and interannually, is required.

Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

“…The low pressure zone in the northern Arabian Peninsula is also captured well by the models, therefore the pressure gradient in the models are quite well, compared with the observed SLP data. The subsidence motion of the Hadley cell during the dry season over the Peninsula, which suppresses convection in the region, may contribute towards this high pressure zone (Evans et al ., 2004).…”

“…The north-eastern side of the Peninsula is bordered by the Arabian Gulf; on the western side the Red Sea is located, whereas the southeastern side is bordered by the Arabian Sea. The Red Sea and the Arabian Gulf are the main sources of water vapour, given the descending motion within the Hadley cell in the region (Evans et al ., 2004). The Sudan low, Mediterranean flow, Indian monsoon and position of the Intertropical Convergence Zone (ITCZ) all control the development of rainfall systems in this region (Atlas, 1984;Walters and Sjoberg, 1988;Zekai and Khalid, 2002;Chakraborty et al ., 2006).…”

The influence of the North Atlantic Oscillation and European circulation regimes on the daily to interannual variability of winter precipitation in Israel

“…This is particularly true in the Middle East, which has a complex climate affected by both tropical and mid-latitude processes (for example Evans, 2004). Previous studies of this region have not explicitly considered the connections between variability on different timescales, instead focusing either on daily variability or on interannual and longer timescales.…”

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

“…Further, they found that the monthly and annual mean frequencies of rain events were satisfactorily reproduced by the model. Evans et al (2004) and Evans (2009) evaluated RCM performance in the Middle East using monthly, annual and seasonal totals of temperature and precipitation. As in Evans et al (2005), the model performance was evaluated against observations using several statistics, including the bias, RMSE and the modified coefficient of efficiency.…”

“…As in Evans et al (2005), the model performance was evaluated against observations using several statistics, including the bias, RMSE and the modified coefficient of efficiency. The RegCM2 RCM which was used in Evans et al (2004) was able to capture the spatial variability of temperature and precipitation better than the European Centre for Medium-Range Weather Forecasts-Tropical Ocean and Global Atmosphere (ECMWF-TOGA) project analyses despite model biases being present. Further, the RCM, based on MM5, used in Evans (2009) showed that the model was able to simulate the precipitation well for most of the domain, while displaying a negative bias in temperature throughout the year.…”