Marek Uliasz

Bridging the gap between atmospheric concentrations and local ecosystem measurements.

This paper demonstrates that atmospheric inversions of CO2 are a reliable tool for estimating regional fluxes. We compare results of an inversion over 18 days and a 300 × 300 km2 domain in southwest France against independent measurements of fluxes from aircraft and towers. The inversion used concentration measurements from 2 towers while the independent data included 27 aircraft transects and 5 flux towers. The inversion reduces the mismatch between prior and independent fluxes, improving both spatial and temporal structures. The present mesoscale atmospheric inversion improves by 30% the CO2 fluxes over distances of few hundreds of km around the atmospheric measurement locations

Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape.

An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 flux over the major croplands of the United States by comparing the results to an inventory of CO2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom-up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion-based mean CO2 -C sink estimates were generally slightly larger, 8-20% for PSU, 10-20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 fluxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region.

Regional and Mesoscale Meteorological Modeling as Applied to Air Quality Studies

Current air quality regulatory models are summarized in Turner et al. (1989). Unfortunately, these models have been applied more to conserve legal consistency over time than to accurately represent atmospheric flow, particularly in complex terrain and coastal zones [see discussions in Moran et al. (1986) and Lyons et al. (1983)]. These regulatory models are also not generally validated on a case-by-case basis but rather by using rank-order correlations (e.g., Fox, 1981).

Application of the Receptor Oriented Approach in Mesoscale Dispersion Modeling

A traditional source oriented approach in dispersion modeling consists in solving model equations forward in time for given sources of pollutant (Figure 1). As a result time and space dependent concentration fields C are obtained. In order to investigate another variant of emission Q, the solution of the model equations must be repeated. In many practical applications, air pollution at a given receptor is of primary interest and an alternative receptor oriented modeling should be considered as a more effective approach. In this case, air quality at the receptor is characterized by an integral of pollution concentration over the modeling domain and time of simulation Φ[C]. The integral can be defined in accordance to the aim of the study using a receptor function R. Instead of concentration, an influence function C* is calculated which provides information on contributions from different sources to air pollution at the receptor.

Influence of Landscape Variability on Atmospheric Dispersion

Using idealized distributions of surface moisture, it is shown that with a significant synoptic prevailing flow over these regions of surface variability, dispersion is generally enhanced over heterogeneous surfaces as compared to horizontally homogeneous conditions. The importance of the heterogeneity becomes less, however, as the large scale wind speed increases and/or the spatial scale of the heterogeneities become less. This work also clearly demonstrates that the use of Gaussian regulatory models in areas of landscape variability is inappropriate. Even casual observers of landscape as viewed from aircraft discern the significant spatial heterogeneity of the surface. Over settled areas, the earths surface is a patchwork of farms, suburban developments, parks, industrial complexes, and so forth. In more remote areas, the ground is generally characterized by varying surface geologic features and vegetation community composition. Superimposed on both natural and man-made landscapes are terrain elevation and aspect variability. In this paper we use examples to: • Document observationally existing natural and anthropogenically modified spatial variability of landscape. * Demonstrate, using a mesoscale meteorological model coupled with a Lagrangian dispersion model, how atmospheric dispersion patterns are modified from what would occur in the absence of these landscape variations.

Large-Eddy Simulation of Air Pollution Dispersion in the Nocturnal Cloud- Topped Atmospheric Boundary Layer

Effects of stratocumulus clouds on the dispersion of contaminants are studied in the nocturnal atmospheric boundary layer. The study is based on a large-eddy simulation (LES) model with a bulk parametrization of clouds. Computations include Lagrangian calculations of atmospheric dispersion of a passive tracer released from point sources at various heights above the ground. The results obtained show that the vertical diffusion is non-Gaussian and depends on the location of a source in the boundary layer.

Subgrid-Scale Parameterizations

Mesoscale meteorological models are governed by nonlinear equations of motion, and continuity equations for mass, heat, and water substance. These equations contain information on atmospheric motion and transport over different scales down to the smallest eddies responsible for the viscous dissipation. Since it is not possible to integrate exactly such a set of equations, the modeler has to distinguish between those eddies that can be resolved by a numerical model and the eddies that are not fully resolved computationally and, therefore, are defined as a subgrid-scale process or turbulence. On the other hand, one might not want detailed information about eddies with sizes that cannot be resolved by an available observational system.

Receptor-Oriented Dispersion Modeling: Extension to Nonlinear Pollution Chemistry

Two complementary approaches presented schematically in Figure 1 can be used in air quality studies if linear dispersion models are applied (Uliasz and Pielke, 1991): a traditional source-oriented approach to calculate concentration fields forward in time for given emission sources; a receptor-oriented approach to calculate influence function fields backward in time for a given receptor.

EFFECT OF LAND SURFACE REPRESENTATION ON SIMULATED MESOSCALE POLLUTION DISPERSION

Natural or man-changed land surfaces are usually heterogeneous over the resolvable scale of the mesoscale atmospheric models used to create meteorological input for air pollution dispersion models. Therefore, an assumption of surface homogeneity within one grid cell of the model may not represent the surface forcing accurately. To overcome this problem a new methodology to take into account the subgrid-scale surface forcing was applied in the mesoscale model following the approach proposed by Avissar and Pielke (1989), Kimura (1989) and Claussen (1991). A series of numerical experiments for idealized and real terrain were performed to demonstrate effect of land surface heterogeneity and its representation on mesoscale pollution dispersion.

Application of the Mesoscale Dispersion Modeling System to Investigation of Air Pollution Transport in Southern Poland

Recent advances in computer technology have opened the door for a broad application of sophisticated numerical models for air pollution dispersion in complex terrain. Very promising opportunities for intensive air quality studies and a real time modeling are provided by Lagrangian particle dispersion models linked to 3-D mesoscale meteorological models (Pielke et al., 1991; Lyons et al., 1993; Uliasz, 1993). A real revolution in mesoscale dispersion applications has been introduced by powerful and affordable workstations which can be dedicated to specific tasks. It allows one not only design and perform short case studies but to use these models for extended periods of time as well. An example of such a computationally intensive application on modern workstations is a project MOHAVE (Uliasz et al., 1993) where daily meteorological and dispersion simulations for the southwestern United States are being performed for a year long study. The modeling methodology developed for the project MOHAVE is being applied to regions in southern Poland with very serious air pollution problems.