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We present a climate modeling approach to reproduce the rainfall patterns over Iran due to the climatic forcings during the past 6000 years. The selected periods are simulated using a spatially high-resolved atmosphere General Circulation Model (GCM). Our results show that the winter rainfall patterns over Iran have changed due to the changes in solar insolation with a wetter condition starting around 3 ka BP and reaching its maximum during the Medieval Climate Anomaly ca. 1 ka BP. The rainfall variability can be explained by the changes in atmospheric conditions as a result of changing incoming solar irradiance based on the Milankovitch theory. A shift in the Earth's energy budget leads to the modulation of the West Asian Subtropical Westerly Jet (WASWJ). The investigations support the hypothesis that during the Holocene a northward shift in the WASWJ contributes to the less cyclonic activities over Iran. This brings less moisture into the region during the winter.

Parameter and structure identifications are necessary in any modelling which aims to achieve a generalised model. Although ANFIS (Adaptive Network-based Fuzzy Inference System) employs well-known parameter-identification techniques, it needs to structure identification techniques for the determination of an optimum number of fuzzy rules and the selection of significant input variables from among the candidate input variables. In this study, a new structure identification scheme is developed and introduced, which is simultaneously capable of the selection of significant input variables and the determination of an optimum number of rules. This new structure identification was joined to ANFIS, and this joined modelling framework was applied to the simulation of virtual air-pollution monitoring stations in Berlin. In this study, 18 virtual particulate matter stations were simulated using the particulate matter data of some of the current stations. In other words, the particulate matter monitoring network of Berlin has been intensified. The evaluation of simulated virtual stations shows that, although the uncertainty of daily particulate matter measurement is about 10 percent, the simulated virtual stations can estimate the mean daily particulate matter with less than 10 percent of error. Mean absolute error and root mean square error of the simulations are less than 2.4 and 3.4µg/m3, respectively. The correlation coefficient of the simulation results was more than 0.94. In addition, the range of mean bias error is between −1.0 and 0.5µg/m3, and the range of factor of exceedance is between −14.8 and 10.8 percent. It means that the simulated virtual stations have a small bias. These results demonstrated the appropriate performance of the joined new structure identification scheme and ANFIS for development of a virtual air pollution monitoring network.

The Indian Monsoon Variability during the past Millennium has been simulated with the ECHAM5 model in two different time slices: Medieval Climate Anomaly and the Little Ice Age. The simulations are compared with new centennial-resolving paleo-reconstructions inferred from various well-dated multi-proxies from two core regions, the Himalaya and Central India. A qualitative moisture index is derived from the proxies and compared with simulated moisture anomalies. The reconstructed paleo-hydrological changes between the Little Ice Age and the Medieval Climate Anomaly depict a dipole pattern between Himalaya and Central India, which is also captured by the model. In the Medieval Climate Anomaly the model exhibits stronger (weaker) dipole signals during summer (winter) compared to Little Ice Age. In summer (winter) months of "Medieval Climate Anomaly minus Little Ice Age" the model simulates wetter conditions over eastern (western and central) Himalaya. Over Central India, a simulated weakening of Indian Summer Monsoon during warmer climate is coincident with reconstructed drying signal in the Lonar Lake record. Based on the model simulations, we can differentiate three physical mechanisms which can lead to the moisture anomalies: (i) the western and central Himalaya are influenced by extra-tropical Westerlies during winter, (ii) the eastern Himalaya is affected by summer variations of temperature gradient between Bay of Bengal and Indian subcontinent and by a zonal band of intensified Indian-East Asian monsoon link north of 25 degrees N, and (iii) Central India is dominated by summer sea surface temperature anomalies in the northern Arabian Sea which have an effect on the large-scale advection of moist air masses. The temperatures in the Arabian Sea are linked to the Ind Pacific Warm Pool, which modulates the Indian monsoon strength. (C) 2014 The Authors. Published by Elsevier B.V.

The development of the East African Rift System (EARS) caused by tectonic forcing during the past 20 million years is believed to influence the regional climate in Africa. However, changes in the Earth's orbital parameters also have an influence on climate on these timescales. To analyze the influence of both tectonic and orbital forcing, we applied the non-hydrostatic regional climate model COSMO-CLM (COSMO model in Climate Mode, CCLM). The regional simulations are driven by different global simulations performed with the coupled ocean–atmosphere general circulation model ECHO-G. To analyze the impact of tectonic forcing a different topography representing a possible past stage of the development of the EARS is applied in the model. The results indicate that tectonic forcing has a strong impact on precipitation in Africa caused by circulation changes. To analyze the impact of orbital forcing, which means the impact of changing insolation patterns, we chose configurations of the Earth's orbit that result in significantly different patterns of insolation. One example is the orbital configuration of the last interglacial at 125,000 years before present, when the seasonality of insolation in the Northern Hemisphere was greatly enhanced, whereas in the Southern Hemisphere it was substantially weakened compared to preindustrial conditions. The simulation of this time slice shows a strong impact of orbital forcing on precipitation in large parts of Africa, caused by altered moisture transport. The application of a biome model helps in analyzing the impact of the two forcing factors on vegetation for a more direct comparison with proxy data.

The climate system is excited by changes in the solar forcing caused by two effects: (a) by variations of the solar radiation caused by dynamical processes within the Sun, and (b) by changes in the orbital parameters of the Earth around the Sun. Numerical simulations with a three-dimensional coupled ocean–atmosphere climate model have been performed to investigate the sensitivity of the climate system to both kinds of changes in the forcing. The climate system responds to the (relatively) short term variations of the solar output variations with changes in the surface temperature of up to 2 K, but without any noticeable long lasting effect. The response to the changes in the orbital parameters is more dramatic: dependent if the orbital parameters correspond to the Eemian (a warm phase at around 125 kyr BP) or the one at 115 kyr BP (the onset of the last ice age), the simulation produces a warm state or the initiation of a cold climate. For the Eemian, the simulated climate agrees with the temperature distribution derived from pollen data. For the glacial inception, the model gradually builds up a large snow cover in the northern part of North America. � 2005 COSPAR. Published by Elsevier Ltd. All rights reserved.