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Global climate warming poses a significant challenge to humanity; it is associated with, e.g., rising sea level and declining Arctic sea ice. Increasing extreme events are also considered to be a result of climate warming, and they may have widespread and diverse effects on health, agriculture, economics, and political conflicts. Still, the detection and quantification of climate change, both in observations and climate models, constitute a main focus of the scientific community. Here, we develop an approach based on network and percolation frameworks to study the impacts of climate changes in the past decades using historical models and reanalysis records, and we analyze the expected upcoming impacts using various future global warming scenarios. We find an abrupt transition during the evolution of the climate network, indicating a consistent poleward expansion of the largest cluster that corresponds to the tropical area, as well as the weakening of the strength of links in the tropic. This is found both in the reanalysis data and in the Coupled Model Intercomparison Project Phase 5 (CMIP5) 21st century climate change simulations. The analysis is based on high-resolution surface (2 m) air temperature field records. We discuss the underlying mechanism for the observed expansion of the tropical cluster and associate it with changes in atmospheric circulation represented by the weakening and expansion of the Hadley cell. Our framework can also be useful for forecasting the extent of the tropical cluster to detect its influence on different areas in response to global warming. [ABSTRACT FROM AUTHOR]

Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network "in"-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger inweighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables. [ABSTRACT FROM AUTHOR]

The Dead Sea is the saltiest and lowest terminal lake in the world. Currently, the Dead Sea's water level is dropping by more than 1 m per year, due to excessive use of the water that previously flowed into it. The Dead Sea constitutes a unique environment and is important from economic, environmental, and touristic points of view. The winter deep convection of the Dead Sea and its deep and narrow basin suggest that non-hydrostatic effects may significantly affect its circulation. Despite these factors, the expected non-hydrostatic effects on the circulation of the Dead Sea have not been investigated. Here we perform high resolution (100 m) ocean general circulation model (the MITgcm) simulations of the Dead Sea and show that the non-hydrostatic results are very different from the hydrostatic ones. Specifically, we show that the winter non-hydrostatic simulations resulted in a layer of dense water overlaying slightly lighter water during the several last hours of the night; this convection process involved plumes of heavier sinking water and the entrainment of the plumes. We also studied the effect of the wind stress's diurnal variability and found it to be important, especially during the summer when the wind's variability drastically increased the surface kinetic energy; however, it did not alter the depth density profile. The results presented here may be important for the Dead Sea's potash industry and for the planned Red Sea-Dead Sea canal that aims to stop and, possibly, to increase the level of the Dead Sea using the Red Sea's water. [ABSTRACT FROM AUTHOR]

Complex networks have been used intensively to investigate the flow and dynamics of many natural systems including the climate system. Here, we develop a percolation based measure, the order parameter, to study and quantify climate networks. We find that abrupt transitions of the order parameter usually occur ~1 year before El Niño events, suggesting that they can be used as early warning precursors of El Niño. Using this method, we analyze several reanalysis datasets and show the potential for good forecasting of El Niño. The percolation based order parameter exhibits discontinuous features, indicating a possible relation to the first order phase transition mechanism. [ABSTRACT FROM AUTHOR]