Amir Givati

Quantifying Precipitation Suppression Due to Air Pollution

Urban air pollution and industrial air pollution have been shown qualitatively to suppress rain and snow. Here, precipitation losses over topographical barriers downwind of major coastal urban areas in California and in the land of Israel that amount to 15%‐25% of the annual precipitation are quantified. The suppression occurs mainly in the relatively shallow orographic clouds within the cold air mass of cyclones. The suppression that occurs over the upslope side is coupled with similar percentage enhancement on the much drier downslope side of the hills. The evidence includes significant decreasing trends of the ratio of hill to coast precipitation during the twentieth century in polluted areas in line with the increasing emissions during the same period, whereas no trends are observed in similar nearby pristine areas. The evidence suggests that air-pollution aerosols that are incorporated in orographic clouds slow down cloud-drop coalescence and riming on ice precipitation and hence delay the conversion of cloud water into precipitation. This effect explains the pattern of greatest loss of precipitation at the midlevel of the upwind slopes, smaller losses at the crest, and enhancement at the downslope side of the hills.

Separation between Cloud-Seeding and Air-Pollution Effects

Abstract Enhancement of precipitation by cloud-seeding operations has been reported in many studies around the world in the last several decades. On the other hand, suppression of rain and snow by urban and industrial air pollution recently has been documented and quantified. Here it is shown that the two effects are the opposite sides of the same coin, demonstrating the sensitivity of clouds to anthropogenic aerosols of different kinds. This is done by analyzing the rainfall amounts in northern Israel during the last 53 years and explaining the changes there as the combined opposite effects of precipitation suppression by air pollution and enhancement by glaciogenic cloud seeding. Time series based on precipitation from rain gauges were analyzed for seeded and nonseeded days and periods in the experimental control and the target areas. The response variable is Ro, the orographic enhancement factor, which is the ratio of gauge-measured rainfall in inland hilly areas (500–1000 m) to the rainfall at the upw...

Using the WRF Model in an Operational Streamflow Forecast System for the Jordan River

AbstractThe Weather Research and Forecasting (WRF) model was employed to provide precipitation forecasts during the 2008/09 and 2009/10 winters (wet season) for Israel and the surrounding region where complex terrain dominates. The WRF precipitation prediction has been coupled with the Hydrological Model for Karst Environment (HYMKE) to forecast the upper Jordan River streamflow. The daily WRF precipitation forecasts were verified against the measurements from a dense network of rain gauges in northern and central Israel, and the simulation results using the high-resolution WRF indicated good agreement with the actual measurements. The daily precipitation amount calculated by WRF at rain gauges located in the upper parts of the Jordan River basin showed good agreement with the actual measurements. Numerical experiments were carried out to test the impact of the WRF model resolution and WRF microphysical schemes, to determine an optimal model configuration for this application. Because of orographic forcin...

Possible impacts of anthropogenic aerosols on water resources of the Jordan River and the Sea of Galilee

about 110 million m 3 yr � 1 (about 6.5% of the national water consumption in Israel) is shown to be caused by a decreasing trend in the factor of precipitation enhancement by uplifting on topographic barriers. Previous studies, reviewed here, show that the most probable cause of this decreasing trend is an increasing trend in the concentrations of submicron air pollution particles during the last half century. These particles slow down the conversion of cloud drops into raindrops and snow flakes, thus decreasing precipitation from short-lived clouds such as form in moist air that crosses topographic barriers. This decreasing trend was partially mitigated by cloud seeding for rain enhancement, but apparently, the air pollution dominated and caused a net loss of orographic precipitation. A large portion of the water resources in this semiarid part of the world results from orographic precipitation. Therefore this is an issue with major economic and societal implications, not only to the study area but to many other densely populated parts of the world where the livelihood of the inhabitants depends on water resulting from orographic precipitation, which might be compromised by the air pollution produced by the very people who depend on that water.

Comments on ''Does Air Pollution Really Suppress Precipitation in Israel?''

Alpert et al. in a recent paper challenged the quantification of the suppression of orographic precipitation that was shown in two recent papers by Givati and Rosenfeld to occur in Israel. Their main claim was that the results were determined by the selection of the rain gauges. In this comment, it is demonstrated that when an objective selection of the rain gauges is applied to all of the rain gauges that were used by Alpert et al. and Givati and Rosenfeld, the outcome replicates the results of Givati and Rosenfeld and provides additional insights. At the final account, this comment further enhances the confidence that orographic precipitation has been suppressed over Israel. The direct evidence to the cause is still lacking.

The Advantage of Using International Multimodel Ensemble for Seasonal Precipitation Forecast over Israel

This study analyzes the results of monthly and seasonal precipitation forecasting from seven different global climate forecast models for major basins in Israel within October–April 1982–2010. The six National Multimodel Ensemble (NMME) models and the ECMWF seasonal model were used to calculate an International Multimodel Ensemble (IMME). The study presents the performance of both monthly and seasonal predictions of precipitation accumulated over three months, with respect to different lead times for the ensemble mean values, one per individual model. Additionally, we analyzed the performance of different combinations of models. We present verification of seasonal forecasting using real forecasts, focusing on a small domain characterized by complex terrain, high annual precipitation variability, and a sharp precipitation gradient from west to east as well as from south to north. The results in this study show that, in general, the monthly analysis does not provide very accurate results, even when using the IMME for one-month lead time. We found that the IMME outperformed any single model prediction. Our analysis indicates that the optimal combinations with the high correlation values contain at least three models. Moreover, prediction with larger number of models in the ensemble produces more robust predictions. The results obtained in this study highlight the advantages of using an ensemble of global models over single models for small domain.