scholarly journals Origin, variability and age of biomass burning plumes intercepted during BORTAS-B

2014 ◽  
Vol 14 (6) ◽  
pp. 8723-8752 ◽  
Author(s):  
D. P. Finch ◽  
P. I. Palmer ◽  
M. Parrington
Keyword(s):  
Biomass Burning ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Indirect Evidence ◽  
Strong Relationship ◽  
Fossil Fuel Combustion ◽  
Eastern Canada ◽  
Chemistry Transport Model ◽  
Effective Age ◽  
Black Carbon Aerosol

Abstract. We use the GEOS-Chem atmospheric chemistry transport model to interpret aircraft measurements of carbon monoxide (CO) in biomass burning outflow taken during the 2011 BORTAS-B campaign over eastern Canada. The model has some skill reproducing the observed variability (r = 0.45) but has a negative bias for observations below 100 ppb and a positive bias above 300 ppb. We find that observed CO variations are largely due to NW North American biomass burning, as expected, with smaller and less variable contributions from fossil fuel combustion from eastern Asia and NE North America. To help interpret observed variations of CO we develop an Eulerian effective age of emissions (A) metric, accounting for mixing and chemical decay, which we apply to pyrogenic emissions of CO. We find that during BORTAS-B the age of emissions intercepted over Halifax, Nova Scotia is typically 4–11 days, and on occasion as young as two days. We show that A is typically 1–5 days older than the associated photochemical ages inferred from colocated measurements of different hydrocarbons. We find that the median difference between the age measures (Δτ) in plumes (CH3CN > 150 ppt) peaks at 3–5 days corresponding to a chemical retardation of 50%. We find a strong relationship in plumes between A and Δτ (r2 = 0.60), which is not evident outwith these plumes (r2 = 0.23). We argue that these observed relationships, together with a robust observed relationship between CO and black carbon aerosol during BORTAS-B (r2 > 0.7), form the basis of indirect evidence that aerosols co-emitted with gases during pyrolysis markedly slowed down the plume photochemistry during BORTAS-B with respect to photochemistry at the same latitude and altitude in clear skies.

scholarly journals Origin, variability and age of biomass burning plumes intercepted during BORTAS-B

2014 ◽  
Vol 14 (24) ◽  
pp. 13789-13800 ◽  
Author(s):  
D. P. Finch ◽  
P. I. Palmer ◽  
M. Parrington
Keyword(s):  
Biomass Burning ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Rank Correlation ◽  
Indirect Evidence ◽  
Strong Relationship ◽  
Negative Bias ◽  
Eastern Canada ◽  
Spearman’S Rank Correlation ◽  
Black Carbon Aerosol

Abstract. We use the GEOS-Chem atmospheric chemistry transport model to interpret aircraft measurements of carbon monoxide (CO) in biomass burning outflow taken during the 2011 BORTAS-B campaign over eastern Canada. The model has some skill reproducing the observed variability, with a Spearman's rank correlation rs = 0.65, but has a positive negative bias for observations <100 ppb and a negative bias for observations > 300 ppb. We find that observed CO variations are largely due to fires over Ontario, as expected, with smaller and less variable contributions from fossil fuel combustion from eastern Asia and NE North America. To help interpret observed variations of CO we develop a Eulerian effective physical age of emissions (A) metric, accounting for mixing and chemical decay, which we apply to pyrogenic emissions of CO. We find that during BORTAS-B the age of emissions intercepted over Halifax, Nova Scotia is typically 4–11 days, and on occasion as young as two days. We show that A is typically 1–5 days older than the associated photochemical ages inferred from co-located measurements of different hydrocarbons. We find that the frequency distribution of differences between the age measures (Δτ) in plumes (defined by CH3CN > 150 ppt) peaks at 3 days. This corresponds to a chemical retardation of 50%. We find a strong relationship in biomass burning plumes between A and Δτ (r2 = 0.80), which is not present outwith these plumes (r2 = 0.28). We argue that these observed relationships, together with a robust observed relationship between CO and black carbon aerosol during BORTAS-B (r2 > 0.7), form the basis of indirect evidence that aerosols co-emitted with gases during pyrolysis markedly slowed down the plume photochemistry during BORTAS-B with respect to photochemistry at the same latitude and altitude in clear skies.

scholarly journals Modeling anthropogenically controlled secondary organic aerosols in a megacity: a simplified framework for global and climate models

2011 ◽  
Vol 4 (4) ◽  
pp. 901-917 ◽  
Author(s):  
A. Hodzic ◽  
J. L. Jimenez
Keyword(s):  
Mexico City ◽  
Biomass Burning ◽  
Climate Models ◽  
Transport Model ◽  
Anthropogenic Pollution ◽  
Temporal Variations ◽  
Anthropogenic Sources ◽  
Organic Vapors ◽  
Chemistry Transport Model ◽  
Evolution Of Oxygen

Abstract. A simplified parameterization for secondary organic aerosol (SOA) formation in polluted air and biomass burning smoke is tested and optimized in this work, towards the goal of a computationally inexpensive method to calculate pollution and biomass burning SOA mass and hygroscopicity in global and climate models. A regional chemistry-transport model is used as the testbed for the parameterization, which is compared against observations from the Mexico City metropolitan area during the MILAGRO 2006 field experiment. The empirical parameterization is based on the observed proportionality of SOA concentrations to excess CO and photochemical age of the airmass. The approach consists in emitting an organic gas as lumped SOA precursor surrogate proportional to anthropogenic or biomass burning CO emissions according to the observed ratio between SOA and CO in aged air, and reacting this surrogate with OH into a single non-volatile species that condenses to form SOA. An emission factor of 0.08 g of the lumped SOA precursor per g of CO and a rate constant with OH of 1.25 × 10−11 cm3 molecule−1 s−1 reproduce the observed average SOA mass within 30 % in the urban area and downwind. When a 2.5 times slower rate is used (5 × 10−12 cm3 molecule−1 s−1) the predicted SOA amount and temporal evolution is nearly identical to the results obtained with SOA formation from semi-volatile and intermediate volatility primary organic vapors according to the Robinson et al. (2007) formulation. Our simplified method has the advantage of being much less computationally expensive than Robinson-type methods, and can be used in regions where the emissions of SOA precursors are not yet available. As the aged SOA/ΔCO ratios are rather consistent globally for anthropogenic pollution, this parameterization could be reasonably tested in and applied to other regions. The evolution of oxygen-to-carbon ratio was also empirically modeled and the predicted levels were found to be in reasonable agreement with observations. The potential enhancement of biogenic SOA by anthropogenic pollution, which has been suggested to play a major role in global SOA formation, is also tested using two simple parameterizations. Our results suggest that the pollution enhancement of biogenic SOA could provide additional SOA, but does not however explain the concentrations or the spatial and temporal variations of measured SOA mass in the vicinity of Mexico City, which appears to be controlled by anthropogenic sources. The contribution of the biomass burning to the predicted SOA is less than 10% during the studied period.

scholarly journals The influence of biogenic emissions on upper-tropospheric methanol as revealed from space

2007 ◽  
Vol 7 (24) ◽  
pp. 6119-6129 ◽  
Author(s):  
G. Dufour ◽  
S. Szopa ◽  
D. A. Hauglustaine ◽  
C. D. Boone ◽  
C. P. Rinsland ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Biomass Burning ◽  
Transport Model ◽  
Tropospheric Chemistry ◽  
Biogenic Emissions ◽  
Chemistry Transport Model ◽  
Mixing Ratios ◽  
Oxygenated Compounds ◽  
Global Space ◽  
The Impact

Abstract. The distribution and budget of oxygenated organic compounds in the atmosphere and their impact on tropospheric chemistry are still poorly constrained. Near-global space-borne measurements of seasonally resolved upper tropospheric profiles of methanol (CH3OH) by the ACE Fourier transform spectrometer provide a unique opportunity to evaluate our understanding of this important oxygenated organic species. ACE-FTS observations from March 2004 to August 2005 period are presented. These observations reveal the pervasive imprint of surface sources on upper tropospheric methanol: mixing ratios observed in the mid and high latitudes of the Northern Hemisphere reflect the seasonal cycle of the biogenic emissions whereas the methanol cycle observed in the southern tropics is highly influenced by biomass burning emissions. The comparison with distributions simulated by the state-of-the-art global chemistry transport model, LMDz-INCA, suggests that: (i) the background methanol (high southern latitudes) is correctly represented by the model considering the measurement uncertainties; (ii) the current emissions from the continental biosphere are underestimated during spring and summer in the Northern Hemisphere leading to an underestimation of modelled upper tropospheric methanol; (iii) the seasonal variation of upper tropospheric methanol is shifted to the fall in the model suggesting either an insufficient destruction of CH3OH (due to too weak chemistry and/or deposition) in fall and winter months or an unfaithful representation of transport; (iv) the impact of tropical biomass burning emissions on upper tropospheric methanol is rather well reproduced by the model. This study illustrates the potential of these first global profile observations of oxygenated compounds in the upper troposphere to improve our understanding of their global distribution, fate and budget.

scholarly journals Modelling atmospheric OH-reactivity in a boreal forest ecosystem

2011 ◽  
Vol 11 (18) ◽  
pp. 9709-9719 ◽  
Author(s):  
D. Mogensen ◽  
S. Smolander ◽  
A. Sogachev ◽  
L. Zhou ◽  
V. Sinha ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Inorganic Compounds ◽  
Chemistry Transport Model ◽  
One Dimensional ◽  
Inorganic Species ◽  
Individual Contributions ◽  
The Individual

Abstract. We have modelled the total atmospheric OH-reactivity in a boreal forest and investigated the individual contributions from gas phase inorganic species, isoprene, monoterpenes, and methane along with other important VOCs. Daily and seasonal variation in OH-reactivity for the year 2008 was examined as well as the vertical OH-reactivity profile. We have used SOSA; a one dimensional vertical chemistry-transport model (Boy et al., 2011a) together with measurements from Hyytiälä, SMEAR II station, Southern Finland, conducted in August 2008. Model simulations only account for ~30–50% of the total measured OH sink, and in our opinion, the reason for missing OH-reactivity is due to unmeasured unknown BVOCs, and limitations in our knowledge of atmospheric chemistry including uncertainties in rate constants. Furthermore, we found that the OH-reactivity correlates with both organic and inorganic compounds and increases during summer. The summertime canopy level OH-reactivity peaks during night and the vertical OH-reactivity decreases with height.

scholarly journals Interannual variability of tropospheric composition: the influence of changes in emissions, meteorology and clouds

2010 ◽  
Vol 10 (5) ◽  
pp. 2491-2506 ◽  
Author(s):  
A. Voulgarakis ◽  
N. H. Savage ◽  
O. Wild ◽  
P. Braesicke ◽  
P. J. Young ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Biomass Burning ◽  
Strong Influence ◽  
Transport Model ◽  
Global Scale ◽  
Meteorological Conditions ◽  
Shortwave Radiation ◽  
Chemistry Transport Model ◽  
Attribution Analysis ◽  
Emission Changes

Abstract. We have run a chemistry transport model (CTM) to systematically examine the drivers of interannual variability of tropospheric composition during 1996–2000. This period was characterised by anomalous meteorological conditions associated with the strong El Niño of 1997–1998 and intense wildfires, which produced a large amount of pollution. On a global scale, changing meteorology (winds, temperatures, humidity and clouds) is found to be the most important factor driving interannual variability of NO2 and ozone on the timescales considered. Changes in stratosphere-troposphere exchange, which are largely driven by meteorological variability, are found to play a particularly important role in driving ozone changes. The strong influence of emissions on NO2 and ozone interannual variability is largely confined to areas where intense biomass burning events occur. For CO, interannual variability is almost solely driven by emission changes, while for OH meteorology dominates, with the radiative influence of clouds being a very strong contributor. Through a simple attribution analysis for 1996–2000 we conclude that changing cloudiness drives 25% of the interannual variability of OH over Europe by affecting shortwave radiation. Over Indonesia this figure is as high as 71%. Changes in cloudiness contribute a small but non-negligible amount (up to 6%) to the interannual variability of ozone over Europe and Indonesia. This suggests that future assessments of trends in tropospheric oxidizing capacity should account for interannual variability in cloudiness, a factor neglected in many previous studies.

scholarly journals Operational total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

2011 ◽  
Vol 4 (7) ◽  
pp. 1491-1514 ◽  
Author(s):  
P. Valks ◽  
G. Pinardi ◽  
A. Richter ◽  
J.-C. Lambert ◽  
N. Hao ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Optical Absorption Spectroscopy ◽  
Chemistry Transport Model ◽  
Air Mass ◽  
Cloud Properties ◽  
The Pacific ◽  
Tropospheric No2 ◽  
Data Processor ◽  
Radiance Data

Abstract. This paper presents the algorithm for the operational near real time retrieval of total and tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME-2). The retrieval is performed with the GOME Data Processor (GDP) version 4.4 as used by the EUMETSAT Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF). The differential optical absorption spectroscopy (DOAS) method is used to determine NO2 slant columns from GOME-2 (ir)radiance data in the 425–450 nm range. Initial total NO2 columns are computed using stratospheric air mass factors, and GOME-2 derived cloud properties are used to calculate the air mass factors for scenarios in the presence of clouds. To obtain the stratospheric NO2 component, a spatial filtering approach is used, which is shown to be an improvement on the Pacific reference sector method. Tropospheric air mass factors are computed using monthly averaged NO2 profiles from the MOZART-2 chemistry transport model. An error analysis shows that the random error in the GOME-2 NO2 slant columns is approximately 0.45 × 1015 molec cm−2. As a result of the improved quartz diffuser plate used in the GOME-2 instrument, the systematic error in the slant columns is strongly reduced compared to GOME/ERS-2. The estimated uncertainty in the GOME-2 tropospheric NO2 column for polluted conditions ranges from 40 to 80 %. An end-to-end ground-based validation approach for the GOME-2 NO2 columns is illustrated based on multi-axis MAXDOAS measurements at the Observatoire de Haute Provence (OHP). The GOME-2 stratospheric NO2 columns are found to be in good overall agreement with coincident ground-based measurements at OHP. A time series of the MAXDOAS and the GOME-2 tropospheric NO2 columns shows that pollution episodes at OHP are well captured by GOME-2. Monthly mean tropospheric columns are in very good agreement, with differences generally within 0.5 × 1015 molec cm−2.

scholarly journals Constraining CO<sub>2</sub> emissions from open biomass burning by satellite observations of co-emitted species: a method and its application to wildfires in Siberia

2014 ◽  
Vol 14 (2) ◽  
pp. 3099-3168 ◽  
Author(s):  
I. B. Konovalov ◽  
E. V. Berezin ◽  
P. Ciais ◽  
G. Broquet ◽  
M. Beekmann ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Biomass Burning ◽  
Transport Model ◽  
Regional Scale ◽  
Satellite Observations ◽  
Emission Model ◽  
Chemistry Transport Model ◽  
Conversion Factors ◽  
Fire Emissions ◽  
Global Emission

Abstract. A method to constrain carbon dioxide (CO2) emissions from open biomass burning by using satellite observations of co-emitted species and a chemistry-transport model (CTM) is proposed and applied to the case of wildfires in Siberia. CO2 emissions are assessed by means of an emission model assuming a direct relationship between the biomass burning rate (BBR) and the Fire Radiative Power (FRP) derived from the MODIS measurements. The key features of the method are (1) estimating the FRP-to-BBR conversion factors (α) for different vegetative land cover types by assimilating the satellite observations of co-emitted species into the CTM, (2) optimal combination of the estimates of α derived independently from satellite observations of different species (CO and aerosol in this study), and (3) estimation of the diurnal cycle of the fire emissions directly from the FRP measurements. Values of α for forest and grassland fires in Siberia and their uncertainties are estimated by using the IASI carbon monoxide (CO) retrievals and the MODIS aerosol optical depth (AOD) measurements combined with outputs from the CHIMERE mesoscale chemistry transport model. The constrained CO emissions are validated through comparison of the respective simulations with the independent data of ground based CO measurements at the ZOTTO site. Using our optimal regional-scale estimates of the conversion factors (which are found to be in agreement with the earlier published estimates obtained from local measurements of experimental fires), the total CO2 emissions from wildfires in Siberia in 2012 are estimated to be in the range from 262 to 477 Tg C, with the optimal (maximum likelihood) value of 354 Tg C. Sensitivity test cases featuring different assumptions regarding the injection height and diurnal variations of emissions indicate that the derived estimates of the total CO2 emissions in Siberia are robust with respect to the modelling options (the different estimates vary within less than 10% of their magnitude). The obtained CO2 emission estimates for several years are compared with the independent estimates provided by the GFED3.1 and GFASv1.0 global emission inventories. It is found that our "top-down" estimates for the total annual biomass burning CO2 emissions in the period from 2007 to 2011 in Siberia are by factors of 2.3 and 1.7 larger than the respective bottom-up estimates; these discrepancies cannot be fully explained by uncertainties in our estimates. There are also considerable differences in the spatial distribution of the different emission estimates; some of those differences have a systematic character and require further analysis.

scholarly journals Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees

2020 ◽  
Vol 20 (13) ◽  
pp. 8063-8082 ◽  
Author(s):  
Peter D. Ivatt ◽  
Mathew J. Evans
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Mean Square ◽  
Model Bias ◽  
Chemistry Transport Model ◽  
Pearson's R ◽  
Process Based Models

Abstract. Predictions from process-based models of environmental systems are biased, due to uncertainties in their inputs and parameterizations, reducing their utility. We develop a predictor for the bias in tropospheric ozone (O3, a key pollutant) calculated by an atmospheric chemistry transport model (GEOS-Chem), based on outputs from the model and observations of ozone from both the surface (EPA, EMEP, and GAW) and the ozone-sonde networks. We train a gradient-boosted decision tree algorithm (XGBoost) to predict model bias (model divided by observation), with model and observational data for 2010–2015, and then we test the approach using the years 2016–2017. We show that the bias-corrected model performs considerably better than the uncorrected model. The root-mean-square error is reduced from 16.2 to 7.5 ppb, the normalized mean bias is reduced from 0.28 to −0.04, and Pearson's R is increased from 0.48 to 0.84. Comparisons with observations from the NASA ATom flights (which were not included in the training) also show improvements but to a smaller extent, reducing the root-mean-square error (RMSE) from 12.1 to 10.5 ppb, reducing the normalized mean bias (NMB) from 0.08 to 0.06, and increasing Pearson's R from 0.76 to 0.79. We attribute the smaller improvements to the lack of routine observational constraints for much of the remote troposphere. We show that the method is robust to variations in the volume of training data, with approximately a year of data needed to produce useful performance. Data denial experiments (removing observational sites from the algorithm training) show that information from one location (for example Europe) can reduce the model bias over other locations (for example North America) which might provide insights into the processes controlling the model bias. We explore the choice of predictor (bias prediction versus direct prediction) and conclude both may have utility. We conclude that combining machine learning approaches with process-based models may provide a useful tool for improving these models.

Sign in / Sign up

Export Citation Format

Share Document