Vertical distribution of PM2.5 and interactions with the atmospheric boundary layer during the development stage of a heavy haze pollution event

Vertical profiles of PM_2.5 (i.e., particulate matter with an aerodynamic diameter of 2.5 µm or less) and meteorological variables (e.g., potential temperature, specific humidity) are crucial to understand formation mechanism including accumulation and dispersion process of PM_2.5, as well as interactions between aerosols and the atmospheric boundary layer (ABL). In this study, vertical distributions of PM_2.5 are characterized through comprehensive analyses of vertical profiles measured by unmanned aerial vehicle (UAV), Micro Pulse LiDAR, and other surface observational data of a heavy aerosol pollution episode occurring on December 22-25, 2017 in Nanjing, China. Results show that PM_2.5 profiles are characterized by a clear three-layer structure with near constant within the mixed layer, a transition layer with a large local gradient in the entrainment zone, and a layer with low concentration and small gradient in the free atmosphere, which shows a large similarity to that of specific humidity. The accumulation of aerosols is found near top of the ABL with the largest increase rate. Vertical distributions of PM_2.5 and their evolution are largely constrained by the ABL thermodynamics during daytime, but show much less dependence on the ABL evolution at nighttime. PM_2.5 provides an important feedback on the nocturnal boundary layer (NBL) leading to significant modification of vertical distributions of potential temperature and water vapor. Moreover, this study suggests that the current boundary layer parameterization scheme needs refinement with aerosol radiative effect included to further improve the ABL height (ABLH) and air quality predictions.

Meteorological modeling relevant to mesoscale and regional air quality applications: a review

The highest correlative relations for air pollution levels are often with meteorological variables such as temperature and wind speed. Today, sophisticated gridded high-resolution meteorological models are used to produce meteorological fields that drive chemical transport models for air quality management. Errors in specification of the physical atmosphere such as temperature, clouds and winds can affect the air quality predictions. Additionally, the efficiency and efficacy of emission control strategies can be compromised by errors in the meteorological fields. In this paper, the role of meteorology in air quality behavior, primarily from the viewpoint of regional ozone modeling as carried out in the U.S., is reviewed. Particular attention is given to physics and new techniques for improving meteorological model performance. Uncertainties in model turbulent mixing in the nighttime boundary layer, where large model differences exist, are examined. The role of spatial mesoscale features such as topography and land/water systems in models are discussed. The nocturnal low-level jet, a mesoscale temporal and spatial feature, and its impact on air quality are examined. Traditional air quality concerns have focused on synoptic conditions at the center of high-pressure systems. However, high ozone levels have also been associated with stationary fronts. The ability of models to capture mesoscale structure and yet retain synoptic structure and its timing is challenging. Data assimilation and its ability to improve model performance are examined. Particular attention is given to vertical nudging strategies that can affect formation of the nocturnal low-level jets. Finally, clouds can have a major impact on air quality since insolation impacts temperature, biogenic emissions and photolysis rates and extremes in stability. Traditional techniques, which attempt to insert cloud water where there is not dynamical support, can lead to additional errors. New dynamical approaches for improving model cloud performance are discussed.Implications: This article shows that there has been a considerable improvement in meteorological models used for air quality simulations. In particular, improvement in the tools for incorporating both traditional observations and new satellite data for retrospective studies has been beneficial to air quality community. However, while this trend is continuing, many challenges remain. As an example, due to having many options available in configuring a model simulation, there is a need to evaluate and recommend sets of options that provide important performance measures.

Modeling Migratory Flight in the Spruce Budworm: Circadian Rhythm

The crepuscular (evening) circadian rhythm of adult spruce budworm (Choristoneura fumiferana (Clem.)) flight activity under the influence of changing evening temperatures is described using a mathematical model. This description is intended for inclusion in a comprehensive model of spruce budworm flight activity leading to the simulation of mass migration events. The model for the temporal likelihood of moth emigration flight is calibrated using numerous observations of flight activity in the moth’s natural environment. Results indicate an accurate description of moth evening flight activity using a temporal function covering the period around sunset and modified by evening temperature conditions. The moth’s crepuscular flight activity is typically coincident with the evening transition of the atmospheric boundary layer from turbulent daytime to stable nocturnal conditions. The possible interactions between moth flight activity and the evening boundary layer transition, with favorable wind and temperature conditions leading to massive and potentially successful migration events, as well as the potential impact of climate change on this process, are discussed.

Surface-Atmosphere Coupling Scale, the Fate of Water, and Ecophysiological Function in a Brazilian Forest

Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin-wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface-atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the "fate of water," or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.

From Near-Neutral to Strongly Stratified: Adequately Modelling the Clear-Sky Nocturnal Boundary Layer at Cabauw

The performance of an atmospheric single-column model (SCM) is studied systematically for stably-stratified conditions. To this end, 11 years (2005-2015) of daily SCM simulations were compared to observations from the Cabauw observatory, The Netherlands. Each individual clear-sky night was classified in terms of the ambient geostrophic wind speed with a [Formula: see text] bin-width. Nights with overcast conditions were filtered out by selecting only those nights with an average net radiation of less than [Formula: see text]. A similar procedure was applied to the observational dataset. A comparison of observed and modelled ensemble-averaged profiles of wind speed and potential temperature and time series of turbulent fluxes showed that the model represents the dynamics of the nocturnal boundary layer (NBL) at Cabauw very well for a broad range of mechanical forcing conditions. No obvious difference in model performance was found between near-neutral and strongly-stratified conditions. Furthermore, observed NBL regime transitions are represented in a natural way. The reference model version performs much better than a model version that applies excessive vertical mixing as is done in several (global) operational models. Model sensitivity runs showed that for weak-wind conditions the inversion strength depends much more on details of the land-atmosphere coupling than on the turbulent mixing. The presented results indicate that in principle the physical parametrizations of large-scale atmospheric models are sufficiently equipped for modelling stably-stratified conditions for a wide range of forcing conditions.

Desert Amplification in a Warming Climate

Here I analyze the observed and projected surface temperature anomalies over land between 50°S-50°N for the period 1950–2099 by large-scale ecoregion and find strongest warming consistently and persistently seen over driest ecoregions such as the Sahara desert and the Arabian Peninsula during various 30-year periods, pointing to desert amplification in a warming climate. This amplification enhances linearly with the global mean greenhouse gases(GHGs) radiative forcing and is attributable primarily to a stronger GHGs-enhanced downward longwave radiation forcing reaching the surface over drier ecoregions as a consequence of a warmer and thus moister atmosphere in response to increasing GHGs. These results indicate that desert amplification may represent a fundamental pattern of global warming associated with water vapor feedbacks over land in low- and mid- latitudes where surface warming rates depend inversely on ecosystem dryness. It is likely that desert amplification might involve two types of water vapor feedbacks that maximize respectively in the tropical upper troposphere and near the surface over deserts, with both being very dry and thus extremely sensitive to changes of water vapor.

Sampling biases in datasets of historical mean air temperature over land

Global mean surface air temperature (T_a) has been reported to have risen by 0.74°C over the last 100 years. However, the definition of mean T_a is still a subject of debate. The most defensible definition might be the integral of the continuous temperature measurements over a day (T_d0). However, for technological and historical reasons, mean T_a over land have been taken to be the average of the daily maximum and minimum temperature measurements (T_d1). All existing principal global temperature analyses over land rely heavily on T_d1. Here, I make a first quantitative assessment of the bias in the use of T_d1 to estimate trends of mean T_a using hourly T_a observations at 5600 globally distributed weather stations from the 1970s to 2013. I find that the use of T_d1 has a negligible impact on the global mean warming rate. However, the trend of T_d1 has a substantial bias at regional and local scales, with a root mean square error of over 25% at 5° × 5° grids. Therefore, caution should be taken when using mean T_a datasets based on T_d1 to examine high resolution details of warming trends.