A 15-year record of CO emissions constrained by MOPITT CO observations

Jiang, Zhe; Worden, J.; Worden, H. M.; Deeter, Merritt N.; Jones, Dylan B. A.; Arellano, A. F.; Henze, D. K.

doi:10.5194/acp-17-4565-2017

Cited by 116 publications

(211 citation statements)

References 77 publications

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“…The central region of South America experiences variable human influence from burning in the arc of deforestation and consequently sees larger IAV in CO compared to the southern region. There is also a recent decrease in the magnitude of positive anomalies over South America (supporting information Figure S4) that could be a response to a slow down in deforestation on the continent, mainly in Brazil (Austin et al, ; Jiang et al, ; Reddington et al, ).…”

Section: Iav In Comentioning

confidence: 99%

Links Between Carbon Monoxide and Climate Indices for the Southern Hemisphere and Tropical Fire Regions

et al. 2018

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In the Southern Hemisphere and tropics, the main contribution to carbon monoxide (CO) variability is from fire emissions, which are connected to climate through the availability, type, and dryness of fuel. Here we assess the data‐driven relationships between CO and climate, aiming to predict atmospheric loading during fire seasons. Observations of total column CO from the Measurements Of Pollution In The Troposphere satellite instrument are used to build a record of monthly anomalies between 2001 and 2016, focusing on seven biomass burning regions of the Southern Hemisphere and tropics. With the exception of 2015, the range of absolute variability in CO is similar between regions. We model CO anomalies in each of the regions using climate indices for the climate modes: El Niño–Southern Oscillation, Indian Ocean Dipole, Tropical South Atlantic, and Antarctic Oscillation. Stepwise forward and backward variable selection is used to choose from statistical regression models that use combinations of climate indices, at lag times between 1 and 8 months relative to CO anomalies. The Bayesian information criterion selects models with the best predictive power. We find that all climate mode indices are required to model CO in each region, generally explaining over 50% of the variability and over 70% for tropical regions. First‐order interaction terms of the climate modes are necessary, producing greatly improved explanation of CO variability over single terms. Predictive capability is assessed for the Maritime Southeast Asia and the predicted peak CO anomaly in 2015 is within 20% of the measurements.

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Section: Iav In Comentioning

confidence: 99%

Links Between Carbon Monoxide and Climate Indices for the Southern Hemisphere and Tropical Fire Regions

et al. 2018

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“…[OH] fields are saved from a prior run of the standard full chemistry simulation. Previously, the CO-only simulation used OH from earlier versions of the model, typically v5-07-08 (e.g., Kopacz et al, 2010;Fisher et al, 2010;Jiang et al, 2011Jiang et al, , 2017, to mitigate a known OH high bias in more recent versions of GEOS-Chem. Here, we use OH from v9-01-03 in both our base and improved CO-only simulations to maintain consistency with the full chemistry model and to ensure all changes are due to the new representation of chemical production rather than differences in OH.…”

Section: Co-only Simulation In Geos-chemmentioning

confidence: 99%

Improved method for linear carbon monoxide simulation and source attribution in atmospheric chemistry models illustrated using GEOS-Chem v9

et al. 2017

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Abstract. Carbon monoxide (CO) simulation in atmospheric chemistry models is frequently used for source-receptor analysis, emission inversion, interpretation of observations, and chemical forecasting due to its computational efficiency and ability to quantitatively link simulated CO burdens to sources. While several methods exist for modeling CO source attribution, most are inappropriate for regions where the CO budget is dominated by secondary production rather than direct emissions. Here, we introduce a major update to the linear CO-only capability in the GEOS-Chem chemical transport model that for the first time allows sourceregion tagging of secondary CO produced from oxidation of non-methane volatile organic compounds. Our updates also remove fundamental inconsistencies between the CO-only simulation and the standard full chemistry simulation by using consistent CO production rates in both. We find that relative to the standard chemistry simulation, CO in the original CO-only simulation was overestimated by more than 100 ppb in the model surface layer and underestimated in outflow regions. The improved CO-only simulation largely resolves these discrepancies by improving both the magnitude and location of secondary production. Despite large differences between the original and improved simulations, however, model evaluation with the global dataset used to benchmark GEOS-Chem shows negligible change to the model's ability to match the observations. This suggests that the current GEOS-Chem benchmark is not well suited to evaluate model changes in regions influenced by biogenic emissions and chemistry, and expanding the dataset to include observations from biogenic source regions (including those from recent aircraft campaigns) should be a priority for the GEOSChem community. Using Australasia as a case study, we show that the new ability to geographically tag secondary CO production provides significant added value for interpreting observations and model results in regions where primary CO emissions are low. Secondary production dominates the CO budget across much of the world, especially in the Southern Hemisphere, and we recommend future model-observation and multi-model comparisons implement this capability to provide a more complete understanding of CO sources and their variability.

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“…So far, satellite retrievals of CO have been used mainly in global-scale analyses, quantifying large-scale CO emissions (e.g., Hooghiemstra et al, 2012a, b;van Leeuwen et al, 2013;Girach and Nair, 2014;Yin et al, 2015;Jiang et al, 2017) with a primary interest in biomass burning. Furthermore, the first attempts have been made to use MOPITT CO retrievals to estimate emission changes over cities (Pommier et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

et al. 2017

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Abstract. The growth of mega-cities leads to air quality problems directly affecting the citizens. Satellite measurements are becoming of higher quality and quantity, which leads to more accurate satellite retrievals of enhanced air pollutant concentrations over large cities. In this paper, we compare and discuss both an existing and a new method for estimating urban-scale trends in CO emissions using multiyear retrievals from the MOPITT satellite instrument. The first method is mainly based on satellite data, and has the advantage of fewer assumptions, but also comes with uncertainties and limitations as shown in this paper. To improve the reliability of urban-to-regional scale emission trend estimation, we simulate MOPITT retrievals using the Weather Research and Forecast model with chemistry core (WRFChem). The difference between model and retrieval is used to optimize CO emissions in WRF-Chem, focusing on the city of Madrid, Spain. This method has the advantage over the existing method in that it allows both a trend analysis of CO concentrations and a quantification of CO emissions. Our analysis confirms that MOPITT is capable of detecting CO enhancements over Madrid, although significant differences remain between the yearly averaged model output and satellite measurements (R 2 = 0.75) over the city. After optimization, we find Madrid CO emissions to be lower by 48

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A 15-year record of CO emissions constrained by MOPITT CO observations

Cited by 116 publications

References 77 publications

Links Between Carbon Monoxide and Climate Indices for the Southern Hemisphere and Tropical Fire Regions

Links Between Carbon Monoxide and Climate Indices for the Southern Hemisphere and Tropical Fire Regions

Improved method for linear carbon monoxide simulation and source attribution in atmospheric chemistry models illustrated using GEOS-Chem v9

Quantification of CO emissions from the city of Madrid using MOPITT satellite retrievals and WRF simulations

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