scholarly journals Seasonal variability of surface and column carbon monoxide over the megacity Paris, high-altitude Jungfraujoch and Southern Hemispheric Wollongong stations

2016 ◽  
Vol 16 (17) ◽  
pp. 10911-10925 ◽  
Author(s):  
Yao Té ◽  
Pascal Jeseck ◽  
Bruno Franco ◽  
Emmanuel Mahieu ◽  
Nicholas Jones ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Northern Hemisphere ◽  
Seasonal Variability ◽  
Transport Model ◽  
Time Lag ◽  
Chemical Transport ◽  
Emission Sources ◽  
Chemical Transport Model ◽  
Surface Measurements ◽  
Total Column

Abstract. This paper studies the seasonal variation of surface and column CO at three different sites (Paris, Jungfraujoch and Wollongong), with an emphasis on establishing a link between the CO vertical distribution and the nature of CO emission sources. We find the first evidence of a time lag between surface and free tropospheric CO seasonal variations in the Northern Hemisphere. The CO seasonal variability obtained from the total columns and free tropospheric partial columns shows a maximum around March–April and a minimum around September–October in the Northern Hemisphere (Paris and Jungfraujoch). In the Southern Hemisphere (Wollongong) this seasonal variability is shifted by about 6 months. Satellite observations by the IASI–MetOp (Infrared Atmospheric Sounding Interferometer) and MOPITT (Measurements Of Pollution In The Troposphere) instruments confirm this seasonality. Ground-based FTIR (Fourier transform infrared) measurements provide useful complementary information due to good sensitivity in the boundary layer. In situ surface measurements of CO volume mixing ratios at the Paris and Jungfraujoch sites reveal a time lag of the near-surface seasonal variability of about 2 months with respect to the total column variability at the same sites. The chemical transport model GEOS-Chem (Goddard Earth Observing System chemical transport model) is employed to interpret our observations. GEOS-Chem sensitivity runs identify the emission sources influencing the seasonal variation of CO. At both Paris and Jungfraujoch, the surface seasonality is mainly driven by anthropogenic emissions, while the total column seasonality is also controlled by air masses transported from distant sources. At Wollongong, where the CO seasonality is mainly affected by biomass burning, no time shift is observed between surface measurements and total column data.

scholarly journals Seasonal variability of surface and column carbon monoxide over megacity Paris, high altitude Jungfraujoch and Southern Hemispheric Wollongong stations

2016 ◽  
Author(s):  
Yao Té ◽  
Pascal Jeseck ◽  
Bruno Franco ◽  
Emmanuel Mahieu ◽  
Nicholas Jones ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Carbon Monoxide ◽  
Seasonal Variability ◽  
Transport Model ◽  
Time Lag ◽  
Time Shift ◽  
Emission Sources ◽  
Near Surface ◽  
Key Species ◽  
Total Column

Abstract. Carbon monoxide (CO) is an atmospheric key species due to its toxicity and its impact on the atmospheric oxidizing capacity, both factors affecting air quality. The paper studies the altitude dependent seasonal variability of CO at the three different sites Paris, Jungfraujoch and Wollongong, with an emphasis on establishing a link between the CO vertical distribution and the nature of CO emission sources. The CO seasonal variability obtained from the total columns and from the free tropospheric partial columns shows a maximum around March-April and a minimum around September-October in the Northern Hemisphere (Paris and Jungfraujoch). In the Southern Hemisphere (Wollongong) this seasonal variability is shifted by about 6 months. Satellite observations by IASI-MetOp and MOPITT instruments confirm this seasonality. Ground-based FTIR is demonstrated to provide useful complementary information due to good sensitivity in the boundary layer. In situ surface measurements of CO volume mixing ratios in Paris and at Jungfraujoch reveal a time-lag of the near surface seasonal variability of about 2 months with respect to the total column variability at the same sites. The chemical transport model GEOS-Chem is employed to interpret our observations. GEOS-Chem sensitivity runs allow identifying the emission sources influencing the seasonal cycle of CO. In Paris and on top of Jungfraujoch, the surface seasonality is mainly driven by anthropogenic emissions, while the total column seasonality is also controlled by air masses transported from distant sources. In the case of Wollongong, where the CO seasonality is mainly affected by biomass burning, no time shift is observed between surface and above the boundary layer.

scholarly journals Statistical diagnostic and correction of a chemistry-transport model for the prediction of total column ozone

2006 ◽  
Vol 6 (2) ◽  
pp. 525-537 ◽  
Author(s):  
S. Guillas ◽  
G. C. Tiao ◽  
D. J. Wuebbles ◽  
A. Zubrow
Keyword(s):  
Transport Model ◽  
Chemical Transport ◽  
Satellite System ◽  
Chemical Transport Model ◽  
Quasi Biennial Oscillation ◽  
Data Set ◽  
Total Column Ozone ◽  
Noise Component ◽  
Column Ozone ◽  
Total Column

Abstract. In this paper, we introduce a statistical method for examining and adjusting chemical-transport models. We illustrate the findings with total column ozone predictions, based on the University of Illinois at Urbana-Champaign 2-D (UIUC 2-D) chemical-transport model of the global atmosphere. We propose a general diagnostic procedure for the model outputs in total ozone over the latitudes ranging from 60° South to 60° North to see if the model captures some typical patterns in the data. The method proceeds in two steps to avoid possible collinearity issues. First, we regress the measurements given by a cohesive data set from the SBUV(/2) satellite system on the model outputs with an autoregressive noise component. Second, we regress the residuals of this first regression on the solar flux, the annual cycle, the Antarctic or Arctic Oscillation, and the Quasi Biennial Oscillation. If the coefficients from this second regression are statistically significant, then they mean that the model did not simulate properly the pattern associated with these factors. Systematic anomalies of the model are identified using data from 1979 to 1995, and statistically corrected afterwards. The 1996–2003 validation sample confirms that the combined approach yields better predictions than the direct UIUC 2-D outputs.

scholarly journals Episodes of cross-polar transport in the Arctic troposphere during July 2008 as seen from models, satellite, and aircraft observations

2010 ◽  
Vol 10 (11) ◽  
pp. 26361-26410 ◽  
Author(s):  
H. Sodemann ◽  
M. Pommier ◽  
S. R. Arnold ◽  
S. A. Monks ◽  
K. Stebel ◽  
...  
Keyword(s):  
Transport Model ◽  
Chemical Transport ◽  
The Arctic ◽  
Actual State ◽  
Chemical Transport Model ◽  
Horizontal Structure ◽  
Model Simulations ◽  
Aircraft Observations ◽  
Total Column ◽  
Aircraft Data

Abstract. During the POLARCAT summer campaign in 2008, two episodes (2–5 July and 7–10 July 2008) occurred where low-pressure systems traveled from Siberia across the Arctic Ocean towards the North Pole. The two cyclones had extensive smoke plumes embedded in their associated air masses, creating an excellent opportunity to use satellite and aircraft observations to validate the performance of atmospheric transport models in the Arctic, which is a challenging model domain due to numerical and other complications. Here we compare transport simulations of carbon monoxide (CO) from the Lagrangian transport model FLEXPART, the Eulerian chemical transport model TOMCAT, and for numerical aspects the limited-area chemical transport model WRF-Chem. Retrievals of total column CO from the IASI passive infrared sensor onboard the MetOp-A satellite are used as a total column CO reference for the two simulations. Main aspect of the comparison is how realistic horizontal and vertical structures are represented in the model simulations. Analysis of CALIPSO lidar curtains and in situ aircraft measurements provide further independent reference points to assess how reliable the model simulations are and what the main limitations are. The horizontal structure of mid-latitude pollution plumes agrees well between the IASI total column CO and the model simulations. However, finer-scale structures are too quickly diffused in the Eulerian models. Aircraft data suggest that the satellite data are biased high, while TOMCAT and WRF-Chem are biased low. FLEXPART fits the aircraft data rather well, but due to added background concentrations the simulation is not independent from observations. The multi-data, multi-model approach allows separating the influences of meteorological fields, model realisation, and grid type on the plume structure. In addition to the very good agreement between simulated and observed total column CO fields, the results also highlight the difficulty to identify a data set that most realistically represents the actual state of the atmosphere.

Spatiotemporal Modeling of Oxidative Potential of Ambient Particulate Matter Using Chemical Transport Model-Derived Emission Sources

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Josephine Bates ◽  
Rodney Weber ◽  
Vishal Verma ◽  
Joseph Abrams ◽  
Ting Fang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Transport Model ◽  
Chemical Transport ◽  
Emission Sources ◽  
Spatiotemporal Modeling ◽  
Ambient Particulate Matter ◽  
Chemical Transport Model ◽  
Oxidative Potential

scholarly journals Towards understanding the variability in biospheric CO<sub>2</sub> fluxes: using FTIR spectrometry and a chemical transport model to investigate the sources and sinks of carbonyl sulfide and its link to CO<sub>2</sub>

2016 ◽  
Vol 16 (4) ◽  
pp. 2123-2138 ◽  
Author(s):  
Yuting Wang ◽  
Nicholas M. Deutscher ◽  
Mathias Palm ◽  
Thorsten Warneke ◽  
Justus Notholt ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Transport Model ◽  
Carbonyl Sulfide ◽  
Co2 Concentration ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Solar Absorption ◽  
Ftir Spectrometry ◽  
Carbon Dioxide Co2 ◽  
Biosphere Model

Abstract. Understanding carbon dioxide (CO2) biospheric processes is of great importance because the terrestrial exchange drives the seasonal and interannual variability of CO2 in the atmosphere. Atmospheric inversions based on CO2 concentration measurements alone can only determine net biosphere fluxes, but not differentiate between photosynthesis (uptake) and respiration (production). Carbonyl sulfide (OCS) could provide an important additional constraint: it is also taken up by plants during photosynthesis but not emitted during respiration, and therefore is a potential means to differentiate between these processes. Solar absorption Fourier Transform InfraRed (FTIR) spectrometry allows for the retrievals of the atmospheric concentrations of both CO2 and OCS from measured solar absorption spectra. Here, we investigate co-located and quasi-simultaneous FTIR measurements of OCS and CO2 performed at five selected sites located in the Northern Hemisphere. These measurements are compared to simulations of OCS and CO2 using a chemical transport model (GEOS-Chem). The coupled biospheric fluxes of OCS and CO2 from the simple biosphere model (SiB) are used in the study. The CO2 simulation with SiB fluxes agrees with the measurements well, while the OCS simulation reproduced a weaker drawdown than FTIR measurements at selected sites, and a smaller latitudinal gradient in the Northern Hemisphere during growing season when comparing with HIPPO (HIAPER Pole-to-Pole Observations) data spanning both hemispheres. An offset in the timing of the seasonal cycle minimum between SiB simulation and measurements is also seen. Using OCS as a photosynthesis proxy can help to understand how the biospheric processes are reproduced in models and to further understand the carbon cycle in the real world.

scholarly journals Validation of urban NO<sub>2</sub> concentrations and their diurnal and seasonal variations observed from the SCIAMACHY and OMI sensors using in situ surface measurements in Israeli cities

2009 ◽  
Vol 9 (12) ◽  
pp. 3867-3879 ◽  
Author(s):  
K. F. Boersma ◽  
D. J. Jacob ◽  
M. Trainic ◽  
Y. Rudich ◽  
I. DeSmedt ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Seasonal Variation ◽  
Transport Model ◽  
Source Region ◽  
Chemical Transport Model ◽  
Data Set ◽  
Near Surface ◽  
Weekly Cycle ◽  
Diurnal Difference ◽  
Surface Measurements

Abstract. We compare a full-year (2006) record of surface air NO2 concentrations measured in Israeli cities to coinciding retrievals of tropospheric NO2 columns from satellite sensors (SCIAMACHY aboard ENVISAT and OMI aboard Aura). This provides a large statistical data set for validation of NO2 satellite measurements in urban air, where validation is difficult yet crucial for using these measurements to infer NOx emissions by inverse modeling. Assuming that NO2 is well-mixed throughout the boundary layer (BL), and using observed average seasonal boundary layer heights, near-surface NO2 concentrations are converted into BL NO2 columns. The agreement between OMI and (13:45) BL NO2 columns (slope=0.93, n=542), and the comparable results at 10:00 h for SCIAMACHY, allow a validation of the seasonal, weekly, and diurnal cycles in satellite-derived NO2. OMI and BL NO2 columns show consistent seasonal cycles (winter NO2 1.6–2.7× higher than summer). BL and coinciding OMI columns both show a strong weekly cycle with 45–50% smaller NO2 columns on Saturday relative to the weekday mean, reflecting the reduced weekend activity, and validating the weekly cycle observed from space. The diurnal difference between SCIAMACHY (10:00) and OMI (13:45) NO2 is maximum in summer when SCIAMACHY is up to 40% higher than OMI, and minimum in winter when OMI slightly exceeds SCIAMACHY. A similar seasonal variation in the diurnal difference is found in the source region of Cairo. The surface measurements in Israel cities confirm this seasonal variation in the diurnal cycle. Using simulations from a global 3-D chemical transport model (GEOS-Chem), we show that this seasonal cycle can be explained by a much stronger photochemical loss of NO2 in summer than in winter.

scholarly journals Seasonal variation of fine and coarse-mode nitrates and related aerosols over East Asia: Synergetic observations and chemical transport model analysis

2017 ◽  
Author(s):  
Itsushi Uno ◽  
Kazuo Osada ◽  
Keiya Yumimoto ◽  
Zhe Wang ◽  
Syuichi Itahashi ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Seasonal Changes ◽  
Transport Model ◽  
Chemical Transport ◽  
Sea Salt ◽  
Large Contribution ◽  
Acid Uptake ◽  
Chemical Transport Model ◽  
Coarse Mode ◽  
Microphysical Processes

Abstract. We analyzed long-term fine- and coarse-mode nitrate and related aerosols (SO42−, NO3−, NH4+, Na+, Ca2+) synergetic observations at Fukuoka (33.52° N, 130.47° E) from August 2014 to October 2015. A Goddard Earth Observing System chemical transport model (GEOS-Chem) including dust and sea-salt acid uptake processes was used to assess the observed seasonal variation, and the impact of long-range transport (LRT) from the Asian continent. For fine aerosols (fSO42−, fNO3−, and fNH4+), numerical results explained the seasonal changes, and a sensitivity analysis excluding Japanese domestic emissions clarified the LRT fraction at Fukuoka (85 % for fSO42−, 47 % for fNO3−, 73 % for fNH4+). Observational data for HNO3, fNO3−, and coarse NO3− (cNO3−) confirmed that cNO3− made up the largest proportion of total nitrate (defined as the sum of fNO3−, cNO3− and HNO3), constituting 40–55 % of total nitrate during the winter, while HNO3− gas constituted approximately 40 % of total nitrate in summer, and fNO3 peaked during the winter. A numerical model reproduced the seasonal variations in fNO3−. For cNO3−, large-scale dust-nitrate outflow from China to Fukuoka was confirmed during all dust events that occurred between January and June. Modeled cNO3− was in good agreement with observations between July and November (mainly coming from sea salt-NO3−). However during the winter, the model underestimated cNO3− levels compared to the observed levels. The reason for this underestimation was examined statistically using multiple regression analysis (MRA). We used cNa+, nss-cCa2+, and cNH4+ as independent variables to describe the observed cNO3− levels; these variables were considered representative of sea salt-cNO3−, dust-cNO3−, and cNO3− accompanied by cNH4+ (cNH4+ term), respectively. The MRA results explained the observed seasonal changes in dust-cNO3− and indicated that the dust-acid uptake scheme reproduced the observed dust-nitrate levels even in winter. The annual average contributions of each component were 43 % (sea salt-cNO3−), 19 % (dust cNO3−), and 38 % (cNH4+ term). The MRA dust-cNO3− component had a high value during the dust season, and the sea salt component made a large contribution throughout the year. During the winter, cNH4+ term made a large contribution. The model did not include aerosol microphysical processes (such as condensation and coagulation between the fine anthropogenic aerosols NO3− and SO42− and coarse particles), and our results suggest that inclusion of aerosol microphysical processes is critical when studying observed cNO3− formation, especially in winter.

scholarly journals Statistical diagnostic and correction of a chemistry-transport model for the prediction of total column ozone

2005 ◽  
Vol 5 (5) ◽  
pp. 10421-10453 ◽  
Author(s):  
S. Guillas ◽  
G. C. Tiao ◽  
D. J. Wuebbles ◽  
A. Zubrow
Keyword(s):  
Transport Model ◽  
Chemical Transport ◽  
Satellite System ◽  
Chemical Transport Model ◽  
Quasi Biennial Oscillation ◽  
Data Set ◽  
Total Column Ozone ◽  
Noise Component ◽  
Column Ozone ◽  
Total Column

Abstract. In this paper, we introduce a statistical method for examining and adjusting chemical-transport models. We illustrate the findings with total column ozone predictions, based on the University of Illinois at Urbana-Champaign 2-D (UIUC 2-D) chemical-transport model of the global atmosphere. We propose a general diagnostic procedure for the model outputs in total ozone over the latitudes ranging from 60° South to 60° North to see if the model captures some typical patterns in the data. The method proceeds in two steps to avoid possible collinearity issues. First, we regress the measurements given by a cohesive data set from the SBUV(/2) satellite system on the model outputs with an autoregressive noise component. Second, we regress the residuals of this first regression on the solar flux, the annual cycle, the Antarctic or Arctic Oscillation, and the Quasi Biennial Oscillation. If the coefficients from this second regression are statistically significant, then they mean that the model did not simulate properly the pattern associated with these factors. Systematic anomalies of the model are identified using data from 1979 to 1995, and statistically corrected afterwards. The 1996–2003 validation sample confirms that the combined approach yields better predictions than the direct UIUC 2-D outputs.

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