Autumn precipitation over Central Vietnam is associated with an increase in the occurrence of tropical cyclones that lead to frequent flooding and pose a significant threat to lives and property. The present analyses reveal a pronounced decadal oscillation of autumn precipitation in Central Vietnam within the 8-11 year frequency band that is modulated by the East Pacific-North Pacific (EP-NP) teleconnection. The negative phase of the EP-NP pattern is associated with a positive sea surface temperature (SST) anomaly in the South China Sea (SCS) that induces low-level convergence, enhances convection, and increases precipitation over Central Vietnam and adjacent islands including Hainan (China) and the Philippines. This circulation feature around the SCS is embedded in a large-scale circulation associated with SST anomalies across the Pacific Ocean-i.e., cooling in the Eastern and Central tropical Pacific sandwiched by warming in the North and South Pacific as well as the Western Pacific Ocean. The positive phase of the EP-NP features opposite SST and circulation anomalies, with the result being reduced rainfall in Central Vietnam. This out-of-phase relationship and shared decadal spectral coherence between the EP-NP index and autumn precipitation in Central Vietnam might be useful for future climate predictions and flood management.

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Technical toxaphene, a broad-spectrum pesticide mixture, degrades in the environment, resulting in potential changes in toxicity. The present study uses a multimedia model that the authors developed to estimate toxaphene degradation in the atmosphere over North America. The predicted degradation has strong spatial and temporal variability determined by processes such as emission and transport of technical toxaphene, as well as the complex interactions among many species (e.g., toxaphene, hydroxyl [OH] radicals, and ozone). More toxaphene is degraded in warmer months due to higher concentrations of technical toxaphene (primarily due to higher technical toxaphene emissions in the southeastern United States and transport to other regions) and OH radicals. In the model, OH radicals are created primarily through the reactions of water vapor with the excited oxygen atom, O( 1 D), generated by the photolysis of ozone, which is produced primarily by reactions of volatile organic compounds and nitrogen oxides (NOx) in the presence of sunlight. The higher OH concentrations in warmer months are primarily the result of higher solar radiation and ozone concentrations. The spatial distribution of degradation depends on the distribution of technical toxaphene soil residues as well as atmospheric transport and chemistry; significant chemical degradation occurs in the southeastern United States where soils are most heavily contaminated by past applications of toxaphene.

Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. Here, we demonstrate a method that can reduce systematic biases in regional climate projections. The global and regional climate models employed to demonstrate this technique are the Community Climate System Model (CCSM) and the Weather Research and Forecasting (WRF) model, respectively. The method first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the CCSM-simulated variables (e.g., temperature, geopotential height, specific humidity, and winds) that are subsequently used to drive the WRF model. The WRF simulations were conducted for the western United States and were driven with a) global reanalysis, b) original CCSM, and c) biascorrected CCSM data. The bias-corrected CCSM data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE) in comparison to the original CCSM-driven WRF simulation. Since most climate applications rely on existing global model output as the forcing data (i.e. they cannot rerun or change the global model), which often contain large biases, this effective and economical method provides a useful tool to reduce biases in regional 2 climate downscaling simulations of water resource variables.