Observations Of Mixing Ratios Research Articles

A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations-a case study of Xuzhou, China.

Carbon monoxide (CO) emission inventory data are crucial for air quality control. However, the emission inventories are labor-intensive and time-consuming and generally have large uncertainties. In this study, we developed a new regional data assimilation system (TracersTracker) for estimating the surface CO emission flux from continuous mixing ratio observations using the proper orthogonal decomposition (POD)-based four-dimensional variational (4D-VAR) data assimilation method (POD-4DVar) and a coupled regional model (Weather Research and Forecasting model (WRF) with the Models-3 Community Multi-scale Air Quality (CMAQ) model). This system was applied to estimate CO emissions in Xuzhou city, China. An experiment was conducted with the continuous hourly surface CO mixing ratio observations from 21 monitoring towers in January and July of 2016. The experimental results of the system were examined and compared with the continuous surface CO observations (a priori emission). We found that the retrieved CO emission fluxes were higher than the a priori emission and were mainly distributed in urban and industrial areas, which were 104% higher in January (winter) and 44% higher in July (summer).

Quantification of carbon dioxide and methane emissions in urban areas: source apportionment based on atmospheric observations

Anthropogenic emissions of carbon dioxide (CO2) and methane (CH4) in the atmosphere constitute an important component of the related carbon budget. The main source of anthropogenic CO2 is burning of fossil fuels, especially in densely populated areas. Similar emissions of CH4 are associated with the agricultural sector, coal mining, and other human activities, such as waste management and storage and natural gas networks supplying methane to large urban, industrial centers. We discuss several methods aimed at characterizing and quantifying atmospheric loads and fluxes of CO2 and CH4 in Krakow, the second largest city in Poland. The methods are based on atmospheric observations of mixing ratios as well as isotopic composition of the investigated gases. Atmospheric mixing ratios of CO2 and CH4 were measured using gas chromatography (GC) and cavity ring-down spectroscopy (CRDS). The isotopic composition of CO2 and CH4 was analyzed using isotope ratio mass spectrometry (IRMS), accelerator mass spectrometry (AMS), and CRDS techniques. These data, combined with auxiliary information characterizing the intensity of vertical mixing in the lower atmosphere (height of the nocturnal boundary layer [NBL] and atmospheric 222Rn concentration), were further used to quantify emission rates of CO2 and CH4 in the urban atmosphere of Krakow. These methods provide an efficient way of quantifying surface emissions of major greenhouse gases originating from distributed sources, thus complementing the widely used bottom-up methodology based on emission statistics.

Modelling the physical multiphase interactions of HNO<sub>3</sub> between snow and air on the Antarctic Plateau (Dome C) and coast (Halley)

Abstract. Emissions of nitrogen oxide (NOx = NO + NO2) from the photolysis of nitrate (NO3−) in snow affect the oxidising capacity of the lower troposphere especially in remote regions of high latitudes with little pollution. Current air–snow exchange models are limited by poor understanding of processes and often require unphysical tuning parameters. Here, two multiphase models were developed from physically based parameterisations to describe the interaction of nitrate between the surface layer of the snowpack and the overlying atmosphere. The first model is similar to previous approaches and assumes that below a threshold temperature, To, the air–snow grain interface is pure ice and above To a disordered interface (DI) emerges covering the entire grain surface. The second model assumes that air–ice interactions dominate over all temperatures below melting of ice and that any liquid present above the eutectic temperature is concentrated in micropockets. The models are used to predict the nitrate in surface snow constrained by year-round observations of mixing ratios of nitric acid in air at a cold site on the Antarctic Plateau (Dome C; 75°06′ S, 123°33′ E; 3233 m a.s.l.) and at a relatively warm site on the Antarctic coast (Halley; 75°35′ S, 26°39′ E; 35 m a.s.l). The first model agrees reasonably well with observations at Dome C (Cv(RMSE) = 1.34) but performs poorly at Halley (Cv(RMSE) = 89.28) while the second model reproduces with good agreement observations at both sites (Cv(RMSE) = 0.84 at both sites). It is therefore suggested that in winter air–snow interactions of nitrate are determined by non-equilibrium surface adsorption and co-condensation on ice coupled with solid-state diffusion inside the grain, similar to Bock et al. (2016). In summer, however, the air–snow exchange of nitrate is mainly driven by solvation into liquid micropockets following Henry's law with contributions to total surface snow NO3− concentrations of 75 and 80 % at Dome C and Halley, respectively. It is also found that the liquid volume of the snow grain and air–micropocket partitioning of HNO3 are sensitive to both the total solute concentration of mineral ions within the snow and pH of the snow. The second model provides an alternative method to predict nitrate concentration in the surface snow layer which is applicable over the entire range of environmental conditions typical for Antarctica and forms a basis for a future full 1-D snowpack model as well as parameterisations in regional or global atmospheric chemistry models.

Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

North American CO<sub>2</sub> exchange: inter-comparison of modeled estimates with results from a fine-scale atmospheric inversion

Abstract. Atmospheric inversion models have the potential to quantify CO2 fluxes at regional, sub-continental scales by taking advantage of near-surface CO2 mixing ratio observations collected in areas with high flux variability. This study presents results from a series of regional geostatistical inverse models (GIM) over North America for 2004, and uses them as the basis for an inter-comparison to other inversion studies and estimates from biospheric models collected through the North American Carbon Program Regional and Continental Interim Synthesis. Because the GIM approach does not require explicit prior flux estimates and resolves fluxes at fine spatiotemporal scales (i.e. 1° × 1°, 3-hourly in this study), it avoids temporal and spatial aggregation errors and allows for the recovery of realistic spatial patterns from the atmospheric data relative to previous inversion studies. Results from a GIM inversion using only available atmospheric observations and a fine-scale fossil fuel inventory were used to confirm the quality of the inventory and inversion setup. An inversion additionally including auxiliary variables from the North American Regional Reanalysis found inferred relationships with flux consistent with physiological understanding of the biospheric carbon cycle. Comparison of GIM results with bottom-up biospheric models showed stronger agreement during the growing relative to the dormant season, in part because most of the biospheric models do not fully represent agricultural land-management practices and the fate of both residual biomass and harvested products. Comparison to earlier inversion studies pointed to aggregation errors as a likely source of bias in previous sub-continental scale flux estimates, particularly for inversions that adjust fluxes at the coarsest scales and use atmospheric observations averaged over long periods. Finally, whereas the continental CO2 boundary conditions used in the GIM inversions have a minor impact on spatial patterns, they have a substantial impact on the continental carbon budget, with a difference of 0.8 PgC yr−1 in the total continental flux resulting from the use of two plausible sets of boundary CO2 mixing ratios. Overall, this inter-comparison study helps to assess the state of the science in estimating regional-scale CO2 fluxes, while pointing towards the path forward for improvements in future top-down and bottom-up modeling efforts.

Relationship of loss, mean age of air and the distribution of CFCs to stratospheric circulation and implications for atmospheric lifetimes

Projections of the recovery of the ozone layer are made with global atmospheric models using a specified time series of mixing ratios of ozone depleting substances (ODSs) at the lower boundary. This time series is calculated using atmospheric mixing ratio observations, emission rates, and an estimate for the atmospheric lifetime. ODS destruction and simulated atmospheric‐lifetime vary among models because they depend on the simulated stratospheric transport and mixing. We investigate the balance between the annual change in ODS burden, its atmospheric loss, and the annual ODS input to the atmosphere using several models. Some models produce realistic distributions for the mean age of air and some do not. Back trajectory calculations relate the fractional release (one minus the amount of ODS at a location relative to its stratospheric entry value) to the mean age through the age spectrum, showing that, for the individual spectrum elements, the maximum altitude and loss increase with age. Models with faster circulations produce “young” distributions for the age of air and fail to reproduce the observed relationship between the mean age of air and the fractional release. Models with realistic mean age of air reproduce the observed relationship. These models yield a lifetime for CFCl3 of ∼56 years, longer than the 45 year lifetime currently used to project future mixing ratios. Use of flux boundary conditions in assessment models would have several advantages, including consistency between the ODS evolution and simulated loss even if the simulated residual circulation changes due to climate change.

Vertical mixing in atmospheric tracer transport models: error characterization and propagation

Abstract. Imperfect representation of vertical mixing near the surface in atmospheric transport models leads to uncertainties in modelled tracer mixing ratios. When using the atmosphere as an integrator to derive surface-atmosphere exchange from mixing ratio observations made in the atmospheric boundary layer, this uncertainty has to be quantified and taken into account. A comparison between radiosonde-derived mixing heights and mixing heights derived from ECMWF meteorological data during May–June 2005 in Europe revealed random discrepancies of about 40% for the daytime with insignificant bias errors, and much larger values approaching 100% for nocturnal mixing layers with bias errors also exceeding 50%. The Stochastic Time Inverted Lagrangian Transport (STILT) model was used to propagate this uncertainty into CO2 mixing ratio uncertainties, accounting for spatial and temporal error covariance. Average values of 3 ppm were found for the 2 month period, indicating that this represents a large fraction of the overall uncertainty. A pseudo data experiment shows that the error propagation with STILT avoids biases in flux retrievals when applied in inversions. The results indicate that flux inversions employing transport models based on current generation meteorological products have misrepresented an important part of the model error structure likely leading to biases in the estimated mean and uncertainties. We strongly recommend including the solution presented in this work: better, higher resolution atmospheric models, a proper description of correlated random errors, and a modification of the overall sampling strategy.

Validation of ILAS v5.2 data with FIRS‐2 balloon observations

Comparisons of remote sensing stratospheric mixing ratio profiles for O3, H2O, N2O, HNO3, and NO2 are shown between data (version 5.2) obtained by the Improved Limb Atmospheric Spectrometer (ILAS) on board the Japanese Advanced Earth Observing Satellite (ADEOS) and the Smithsonian Astrophysical Observatory far‐infrared spectrometer (FIRS)‐2 balloon‐borne spectrometer from a flight on 30 April 1997 originating in Fairbanks, Alaska. Submillimeter wave remote sensing and UV in situ observations of mixing ratios of O3 from Jet Propulsion Laboratory (JPL) instruments obtained on board the same balloon gondola are also used for comparison with ILAS mixing ratios of O3. The remote sensing balloon observations occurred roughly 5° latitude, and the in situ 2° latitude, north of the nearest ILAS observations and were taken between 0700 and 1300 local solar time (LST), while the ILAS data were taken at the previous local sunset (near 2100 LST). Back trajectories from the locations and times of balloon data sets to the prior local sunset were within 2° latitude and 10° longitude of the nearest ILAS occultation; the validation therefore effectively occurred in very nearly the same air mass as was observed with ILAS, and well within a 1‐day time interval. The mixing ratio profiles of all of the compared molecules (O3, HNO3, N2O, H2O, and NO2) agree to within the combined uncertainties with only minor systematic offsets.

On four‐dimensional variational assimilation of ozone data in weather‐prediction models

AbstractRoutinely made satellite observations of total ozone contain information about the dynamical state of the atmosphere in the regions where the ozone gradients are strong. It is argued that four‐dimensional variational assimilation (4D‐VAR) of these data with a meteorological model may be a way of extracting this information in order to use it for determining the initial condition of the model. In the first part of this paper the problem of assimilating ozone data in a variational context is discussed. In the second part a series of simple experiments is presented in which ozone pseudo‐observations are assimilated with a barotropic vorticity‐equation model. The aim of these experiments is to examine whether the atmospheric flow at a given level near the tropopause can be reconstructed by a 4D‐VAR scheme using a series of simulated observations of passive tracer mixing ratios. Both data produced by the assimilating model itself and data generated by a more realistic three‐dimensional model have been tested. It is found that when started from a purely zonal flow field, 4D‐VAR can to a large extent reconstruct the flow field, using only observations of the mixing ratio of the tracer. The quality of the result depends on the resolution of the model and on the choice of the length of the assimilation window.