Effects of Urbanization on the Temperature Inversion Breakup in a Mountain Valley with Implications for Air Quality
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In this article, it is shown that in a mountain valley subject to temperature inversions, urbanization can have an important influence on air quality through effects on the inversion breakup.Abstract:
Many cities located in valleys with limited ventilation experience serious air pollution problems. The ventilation of an urban valley can be limited not only by orographic barriers, but also by urban heat island–induced circulations and/or the capping effect of temperature inversions. Furthermore, land-use/-cover changes caused by urbanization alter the dynamics of temperature inversions and urban heat islands, thereby affecting air quality in an urban valley. By means of idealized numerical simulations, it is shown that in a mountain valley subject to temperature inversions urbanization can have an important influence on air quality through effects on the inversion breakup. Depending on the urban area fraction in the simulations, the breakup time changes, the cross-valley wind system can evolve from a confined to an open system during the daytime, the slope winds can be reversed by the interplay between the urban heat island and the temperature inversion, and the breakup pattern can migrate from ...read more
Citations
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Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations
Abstract: We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.
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