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Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected in the Perdigão experiment are analyzed herein to provide a foundation for improved wind gust characterization and process-level understanding of flow intermittency in complex terrain. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars are used to conduct a detailed study of gust characteristics with a specific focus on the parent distributions of nine gust parameters (that describe velocity, time, and length scales), their joint distributions, height variation, and coherence in the vertical and horizontal planes. Best-fit distributional forms for varying gust properties show good agreement with those from previous experiments in moderately complex terrain but generate nonconservative estimates of the gust properties that are of key importance to structural loading. Probability distributions of gust magnitude derived from vertically pointing Doppler lidars exhibit good agreement with estimates from sonic anemometers despite differences arising from volumetric averaging and the terrain complexity. Wind speed coherence functions during gusty periods (which are important to structural wind loading) are similar to less complex sites for small vertical displacements (10 to 40 m), but do not exhibit an exponential form for larger horizontal displacements (800 to 1500 m).

The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming holes (i.e., locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θe are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (SM). SM is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes, confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in minimum and maximum θe, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme maximum θe. Over the entire domain, the ANN with three hidden layers exhibits high accuracy in predicting maximum θe > 347 K. The median hit rate for maximum θe > 347 K is > 0.60, while the median false alarm rate is ≈ 0.08.

Limited area (regional) models applied at high resolution over specific regions of interest are generally expected to more accurately capture the spatiotemporal variability of key meteorological and climate parameters. However, improved performance is not inevitable, and there remains a need to optimize use of numerical resources and to quantify the impact on simulation fidelity that derives from increased resolution. The application of regional models for climate forcing assessment is currently limited by the lack of studies quantifying the sensitivity to horizontal spatial resolution and the physical–dynamical–chemical schemes driving the simulations. Here we investigate model skill in simulating meteorological, chemical and aerosol properties as a function of spatial resolution, by applying the Weather Research and Forecasting model with coupled Chemistry (WRF-Chem) over eastern North America at different resolutions. Using Brier skill scores and other statistical metrics it is shown that enhanced resolution (from 60 to 12 km) improves model performance for all of the meteorological parameters and gas-phase concentrations considered, in addition to both mean and extreme aerosol optical depth (AOD) in three wavelengths in the visible relative to satellite observations, principally via increase of potential skill. Some of the enhanced model performance for AOD appears to be attributable to improved simulation of meteorological conditions and the concentration of key aerosol precursor gases (e.g., SO2 and NH3). Among other reasons, a dry bias in the specific humidity in the boundary layer and a substantial underestimation of total monthly precipitation in the 60 km simulations are identified as causes for the better performance of WRF-Chem simulations at 12 km.

Assessing the ability of global and regional models to describe aerosol optical properties is essential to reducing uncertainty in aerosol direct radiative forcing in the contemporary climate and to improving confidence in future projections. Here we evaluate the performance of high-resolution simulations conducted using the Weather Research and Forecasting model with coupled with Chemistry (WRF-Chem) in capturing spatiotemporal variability of aerosol optical depth (AOD) and the Ångström exponent (AE) by comparison with ground- and space-based remotely sensed observations. WRF-Chem is run over eastern North America at a resolution of 12 km for a representative year (2008). A systematic positive bias in simulated AOD relative to observations is found (annual mean fractional bias (MFB) is 0.15 and 0.50 relative to MODIS (MODerate resolution Imaging Spectroradiometer) and AERONET, respectively), whereas the spatial variability is well captured during most months. The spatial correlation of observed and simulated AOD shows a clear seasonal cycle with highest correlation during summer months (r = 0.5–0.7) when the aerosol loading is large and more observations are available. The model is biased towards the simulation of coarse-mode aerosols (annual MFB for AE = −0.10 relative to MODIS and −0.59 for AERONET), but the spatial correlation for AE with observations is 0.3–0.5 during most months, despite the fact that AE is retrieved with higher uncertainty from the remote-sensing observations. WRF-Chem also exhibits high skill in identifying areas of extreme and non-extreme aerosol loading, and its ability to correctly simulate the location and relative intensity of extreme aerosol events (i.e., AOD > 75th percentile) varies between 30 and 70 % during winter and summer months, respectively.

Recent laboratory chamber studies indicate a significant role for highly oxidized low-volatility organics in new particle formation (NPF), but the actual role of these highly oxidized low-volatility organics in atmospheric NPF remains uncertain. Here, particle size distributions (PSDs) measured in nine forest areas in North America are used to characterize the occurrence and intensity of NPF and to evaluate model simulations using an empirical formulation in which formation rate is a function of the concentrations of sulfuric acid and low-volatility organics from alpha-pinene oxidation (Nucl-Org), and using an ion-mediated nucleation mechanism (excluding organics) (Nucl-IMN). On average, NPF occurred on ~ 70 % of days during March for the four forest sites with springtime PSD measurements, while NPF occurred on only ~ 10 % of days in July for all nine forest sites. Both Nucl-Org and Nucl-IMN schemes capture the observed high frequency of NPF in spring, but the Nucl-Org scheme significantly overpredicts while the Nucl-IMN scheme slightly underpredicts NPF and particle number concentrations in summer. Statistical analyses of observed and simulated ultrafine particle number concentrations and frequency of NPF events indicate that the scheme without organics agrees better overall with observations. The two schemes predict quite different nucleation rates (including their spatial patterns), concentrations of cloud condensation nuclei, and aerosol first indirect radiative forcing in North America, highlighting the need to reduce NPF uncertainties in regional and global earth system models.

Ultrafine particle size distributions through a deciduous forest canopy indicate that nucleation mode particle concentrations decline with depth into the canopy, such that number concentrations at the bottom of the canopy are an average of 16% lower than those at the top. However, growth rates of nucleation mode particles (diameters 6–30 nm) are invariant with height within the canopy, which implies that the semi-volatile gases contributing to their growth are comparatively well-mixed through the canopy. Growth rates of nucleation mode particles during a meteorological drought year (2012) were substantially lower than during a meteorologically normal year with high soil water potential (2013). This may reflect suppression of actual biogenic volatile organic compound (BVOC) emissions by drought and thus a reduction in the production of condensable products during the drought-affected vegetation season. This hypothesis is supported by evidence that growth rates during the normal year exhibit a positive correlation with emissions of BVOC modeled on observed forest composition, leaf area index, temperature and photosynthetically active radiation (PAR), but particle growth rates during the drought-affected vegetation season are not correlated with modeled BVOC emissions. These data thus provide indirect evidence that drought stress in forests may reduce BVOC emissions and limit growth of nucleation mode particles to climate-relevant sizes.

The Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics & Nitrogen (BEACHON) project seeks to understand the feedbacks and inter-relationships between hydrology, biogenic emissions, carbon assimilation, aerosol properties, clouds and associated feedbacks within water-limited ecosystems. The Manitou Experimental Forest Observatory (MEFO) was established in 2008 by the National Center for Atmospheric Research to address many of the BEACHON research objectives, and it now provides a fixed field site with significant infrastructure. MEFO is a mountainous, semi-arid ponderosa pine-dominated forest site that is normally dominated by clean continental air but is periodically influenced by anthropogenic sources from Colorado Front Range cities. This article summarizes the past and ongoing research activities at the site, and highlights some of the significant findings that have resulted from these measurements. These activities include - soil property measurements; - hydrological studies; - measurements of high-frequency turbulence parameters; - eddy covariance flux measurements of water, energy, aerosols and carbon dioxide through the canopy; - determination of biogenic and anthropogenic volatile organic compound emissions and their influence on regional atmospheric chemistry; - aerosol number and mass distributions; - chemical speciation of aerosol particles; - characterization of ice and cloud condensation nuclei; - trace gas measurements; and - model simulations using coupled chemistry and meteorology. In addition to various long-term continuous measurements, three focused measurement campaigns with state-of-the-art instrumentation have taken place since the site was established, and two of these studies are the subjects of this special issue: BEACHON-ROCS (Rocky Mountain Organic Carbon Study, 2010) and BEACHON-RoMBAS (Rocky Mountain Biogenic Aerosol Study, 2011).

New particle formation has been observed at a number of ground-based measurement sites. Prior research has provided evidence that this new particle formation, while observed in the near-surface layer, is actually occurring in atmospheric layers above the surface and appears to be focused in or close to the residual layer formed by the nocturnal inversion. Here, we present both observations and modeling for southern Indiana which support this postulate. Based on simulations with a detailed aerosol dynamics model and the Weather Research and Forecasting model, along with data from ground-based remote sensing instruments and detailed gas and particle phase measurements, we show evidence that (i) the maximum rate change of ultrafine particle concentrations as observed close to the surface is always preceded by breakdown of the nocturnal inversion and enhancement of vertical mixing and (ii) simulated particle size distributions exhibit greatest accord with surface observations during and subsequent to nucleation only when initialized with a particle size distribution representative of clear atmospheric conditions, rather than the in situ (ground-level) particle size distribution.

Measurements of aerosol particle physical and chemical properties, gas phase concentrations and meteorological parameters were made along a transect in southern Indiana during the Nucleation In ForesTs (NIFTy) experiment conducted in May 2008. These measurements indicate nucleation was observed at all three measurement sites on almost half of all sampling days. The intensity of the nucleation events, as measured by the increase in ≥10 nm aerosol particle number concentrations of approximately 2×104 cm−3 over a layer of at least 300 m depth, is in good agreement with recent model results for the Midwestern USA derived using PMCAMx-UF. During the hour after termination of nucleation approximately half of the number concentration reduction is due to coagulation, while the remainder is due in equal parts to dry deposition and entrainment of relatively ultra-fine aerosol particle free troposphere air. Clear nucleation with continuous subsequent growth is only observed on days when the morning fractional cloud cover was less than 30%. It is associated with a clear transition from a strongly stratified atmosphere with low turbulence intensity and weak vertical velocities, to much a weaker vertical gradient of wind speed, increased turbulence intensity and stronger downwards vertical velocities, consistent with growth of the mixed layer and entrainment of air from the residual layer. Nucleation intensity is not very strongly determined by the prevailing condensational sink. However, there is a strong correlation between both a modified version of the Nucleation Parameter from Boy and Kulmala (2002) and ultrafine aerosol particle number concentrations, and mean morning H2SO4 concentrations and ultrafine aerosol particle number concentrations. Five A-class event days during NIFTy were characterized by values of the dimensionless nucleation parameter of Kuang et al. (2010) that are below 0.3, further indicating the applicability of their postulate that nucleation is favored by LΓ values below 0.7. Based on aerosol particle composition measurements it appears that aerosol particle formation and initial growth to approximately 30 nm diameter is dominated by ammonium and sulfate. Conservative estimates of the percent contribution of H2SO4 to aerosol particle growth (for sub-30 nm aerosol particles) on five A-class event days ranged from 23 to 85%.