Optimizing global CO emission estimates using a four-dimensional variational data assimilation system and surface network observations

We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60 % in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, with the limited amount of data from the surface network, the system becomes data sparse resulting in a large solution space. Sensitivity studies have shown that model uncertainties (e.g., vertical distribution of biomass burning emissions and the OH field) and the prior inventories used, influence the inferred emission estimates. Also, since the observations only constrain total CO emissions, the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10 %. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes

Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations

[1] This paper compares two global inversions to estimate carbon monoxide (CO) emissions for 2004. Either surface flask observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) or CO total columns from the Measurement of Pollution in the Troposphere (MOPITT) instrument are assimilated in a 4D-Var framework. Inferred emission estimates from the two inversions are consistent over the Northern Hemisphere (NH). For example, both inversions increase anthropogenic CO emissions over Europe (from 46 to 94 Tg CO/yr) and Asia (from 222 to 420 Tg CO/yr). In the Southern Hemisphere (SH), three important findings are reported. First, due to their different vertical sensitivity, the stations-only inversion increases SH biomass burning emissions by 108 Tg CO/yr more than the MOPITT-only inversion. Conversely, the MOPITT-only inversion results in SH natural emissions (mainly CO from oxidation of NMVOCs) that are 185 Tg CO/yr higher compared to the stations-only inversion. Second, MOPITT-only derived biomass burning emissions are reduced with respect to the prior which is in contrast to previous (inverse) modeling studies. Finally, MOPITT derived total emissions are significantly higher for South America and Africa compared to the stations-only inversion. This is likely due to a positive bias in the MOPITT V4 product. This bias is also apparent from validation with surface stations and ground-truth FTIR columns. Our results show that a combined inversion is promising in the NH. However, implementation of a satellite bias correction scheme is essential to combine both observational data sets in the SH

Constraining CO emission estimates using atmospheric observations

We apply a four-dimensional variational (4D-Var) data assimilation system to optimize carbon monoxide (CO) emissions and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. In the first study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-Var system. Uncertainty reduction up to 60% in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, since the observations only constrain total CO emissions, the 4D-Var system has difficulties separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10%. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes. In the second study, we compare two global inversions to estimate carbon monoxide (CO) emissions for 2004. Either surface flask observations from NOAA or CO total columns from the MOPITT instrument are assimilated in a 4D-Var framework. In the Southern Hemisphere (SH) three important findings are reported. First, due to their different vertical sensitivity, the stations-only inversion increases SH biomass burning emissions by 108 Tg CO/yr more than the MOPITT-only inversion. Conversely, the MOPITT-only inversion results in SH natural emissions (mainly CO from oxidation of NMVOCs) that are 185 Tg CO/yr higher compared to the stations-only inversion. Second, MOPITT-only derived biomass burning emissions are reduced with respect to the prior which is in contrast to previous (inverse) modeling studies. Finally, MOPITT derived total emissions are significantly higher for South America and Africa compared to the stations-only inversion. This is likely due to a positive bias in the MOPITT V4 product. This bias is also apparent from validation with surface stations and ground-truth FTIR columns. In the final study we present the first inverse modeling study to estimate CO emissions constrained by both surface (NOAA) and satellite (MOPITT) observations using a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations are underestimated in current inventories by 50-100%

Interannual variability of carbon monoxide emission estimates over South America from 2006 to 2010

We present the first inverse modeling study to estimate CO emissions constrained by both surface and satellite observations. Our 4D-Var system assimilates National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) surface and Measurements Of Pollution In The Troposphere (MOPITT) satellite observations jointly by fitting a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. South American dry season (August and September) biomass burning emission estimates amount to 60, 92, 42, 16 and 93 Tg CO/yr for 2006 to 2010, respectively. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations in Sao Paulo state are underestimated in current inventories by 50-100%. We conclude that climatic conditions (such as the widespread drought in 2010) seem the most likely cause for the IAV in biomass burning CO emissions. However, socio-economic factors (such as the growing global demand for soy, beef and sugar cane ethanol) and associated deforestation fires, are also likely as drivers for the IAV of CO emissions, but are difficult to link directly to CO emissions

Correction to "Interannual variability of carbon monoxide emission estimates over South America from 2006 to 2010"

We present the first inverse modeling study to estimate CO emissions constrained by both surface and satellite observations. Our 4D-Var system assimilates National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) surface and Measurements Of Pollution In The Troposphere (MOPITT) satellite observations jointly by fitting a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. South American dry season (August and September) biomass burning emission estimates amount to 60, 92, 42, 16 and 93 Tg CO/yr for 2006 to 2010, respectively. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations in Sao Paulo state are underestimated in current inventories by 50-100%. We conclude that climatic conditions (such as the widespread drought in 2010) seem the most likely cause for the IAV in biomass burning CO emissions. However, socio-economic factors (such as the growing global demand for soy, beef and sugar cane ethanol) and associated deforestation fires, are also likely as drivers for the IAV of CO emissions, but are difficult to link directly to CO emissions

Variabilidade Espaço-Temporal do Monóxido de Carbono Sobre a América do Sul a Partir de Dados de Satélite de 2003 A 2012

Using the information of the Earth Observation System/AQUA (EOS/AQUA) satellite during the 2003-2012 period, the present work investigates the tropospheric carbon monoxide (CO) behavior over the central South American region, and its spatiotemporal variability. The results show a well-defined seasonal behavior of the CO concentration, with decrease during the wet season and increase during dry season, due to the biomass burning increase during this period. Since the biomass burning is directly associated with the climate variability, through the rainfall decrease/increase in the central and eastern South American sector, the CO possesses high variability in central Brazil, the region with the largest number of burning focus. The results also show that the CO variability in the interanual time scale is related to the El Niño/Southern Oscillation (ENSO) phenomenon, such that rainfall reduction (increase) in the central and eastern part of South America during the La Niña (El Niño) onset phase contributes to increase (reduce) the biomass burning and in consequence the CO emission in this region. On the other hand, during the ENSO mature stage, the great rainfall and CO concentration variabilities occur in the northern and northeastern South American regions. The results presented in this paper might be useful for monitoring activities. © 2017, Sociedade Brasileira de Meteorologia. All rights reserved