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Hourly data for sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and inhalable particulate matter (PM 10 ) over a 33-month period from a network of air quality monitoring stations across Qingdao, a major coastal city in eastern China, along with surface and upper-air meteorological data, are used to characterize the spatiotemporal variability of these pollutants in the region and the role of meteorological conditions play in pollution episodes. Large differences in the concentrations of all three pollutants are found between densely populated or industrial areas and suburban commercial or residential or coastal tourist areas, but the differences are relatively small between older and newer parts of the residential-commercial areas and between old and newly developed industrial areas. Wavelet analyses revealed a strong seasonal cycle for all three pollutants, introseasonal variability with a periodicity depending on pollutant and location, and diurnal and a semi-diurnal variability with season-dependent amplitude and phase. Low wind speed is found to be the leading factor for pollution buildup in the region. These results may prove useful for urban planning and development and implementation of effective air pollution control strategies for other coastal regions with economic development similar to Qingdao.

Many studies have shown the importance of anthropogenic greenhouse gas emissions in contributing to observed upward trends in the occurrences of temperature extremes over the U.S. However, few studies have investigated the contributions of internal variability in the climate system to these observed trends. Here we use daily maximum temperature time series from the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset to identify trends in seasonal warm anomalies over the contiguous U.S. in the three most recent decades and explore their relationships to low-frequency modes of internal climate variability. The results reveal substantial upward trends in the frequency of warm anomalies in all seasons and in all regions of the U.S., except for portions of the Intermountain West in winter where significant downward trends occur. The strengths and regional coverage of the trends, however, differ considerably by season. These trends can be explained, in part, by the large-scale anomalous atmospheric circulations associated with low-frequency sea-surface temperature oscillations characterized by the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The association between the upward trends in the seasonal warm anomalies and PDO and AMO is further confirmed by the century-long (1871-2012) Twentieth Century Reanalysis dataset.

The southerly Great Plains low-level jet (GPLLJ) is one of the most significant circulation features of the central U.S. linking large-scale atmospheric circulation with the regional climate. GPLLJs transport heat and moisture, contribute to thunderstorm and severe weather formation, provide a corridor for the springtime migration of birds and insects, enhance wind energy availability, and disperse air pollution. We assess future changes in GPLLJ frequency using an eight member ensemble of dynamically-downscaled climate simulations for the mid-21st century. Nocturnal GPLLJ frequency is projected to increase in the southern plains in spring and in the central plains in summer, whereas current climatological patterns persist into the future for daytime and cool season GPLLJs. The relationship between future GPLLJ frequency and the extent and strength of anticyclonic airflow over eastern North America varies with season. Most simulations project a westward shift of anticyclonic airflow in summer, but uncertainty is larger for spring with only half of the simulations suggesting a westward expansion. The choice of regional climate model and the driving lateral boundary conditions have a large influence on the projected future changes in GPLLJ frequency and highlight the importance of multi-model ensembles to estimate the uncertainty surrounding the future GPLLJ climatology.