Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Satellite observations indicate substantial spatiotemporal variability in biomass burning NOx emission factors for South America
    Castellanos, P. ; Boersma, K.F. ; Werf, G.R. van de - \ 2014
    Atmospheric Chemistry and Physics 14 (2014). - ISSN 1680-7316 - p. 3929 - 3943.
    ozone monitoring instrument - fire emissions - trace gases - tropospheric chemistry - chemical-composition - nitrogen-dioxide - tropical forest - b experiment - model tm5 - brazil
    Biomass burning is an important contributor to global total emissions of NOx (NO+NO2). Generally bottom-up fire emissions models calculate NOx emissions by multiplying fuel consumption estimates with static biome-specific emission factors, defined in units of grams of NO per kilogram of dry matter consumed. Emission factors are a significant source of uncertainty in bottom-up fire emissions modeling because relatively few observations are available to characterize the large spatial and temporal variability of burning conditions. In this paper we use NO2 tropospheric column observations from the Ozone Monitoring Instrument (OMI) from the year 2005 over South America to calculate monthly NOx emission factors for four fire types: deforestation, savanna/grassland, woodland, and agricultural waste burning. In general, the spatial patterns in NOx emission factors calculated in this work are consistent with emission factors derived from in situ measurements from the region but are more variable than published biome-specific global average emission factors widely used in bottom-up fire emissions inventories such as the Global Fire Emissions Database (GFED). Satellite-based NOx emission factors also indicate substantial temporal variability in burning conditions. Overall, we found that deforestation fires have the lowest NOx emission factors, on average 30% lower than the emission factors used in GFED v3. Agricultural fire NOx emission factors were the highest, on average a factor of 1.8 higher than GFED v3 values. For savanna, woodland, and deforestation fires, early dry season NOx emission factors were a factor of ~1.5–2 higher than late dry season emission factors. A minimum in the NOx emission factor seasonal cycle for deforestation fires occurred in August, the time period of severe drought in South America in 2005, supporting the hypothesis that prolonged dry spells may lead to an increase in the contribution of smoldering combustion from large-diameter fuels, offsetting the higher combustion efficiency of dryer fine fuels. We evaluated the OMI-derived NOx emission factors with SCIAMACHY NO2 tropospheric column observations and found improved model performance in regions dominated by fire emissions.
    Dynamic biomass burning emission factors and their impact on atmospheric CO mixing ratios.
    Leeuwen, T.T. van; Peters, W. ; Krol, M.C. ; Werf, G.R. van der - \ 2013
    Journal of Geophysical Research: Atmospheres 118 (2013)12. - ISSN 2169-897X - p. 6797 - 6815.
    transform infrared-spectroscopy - trace gas emissions - zoom model tm5 - carbon-monoxide - southern africa - fire emissions - burned-area - interannual variability - laboratory measurements - smoldering combustion
    [1] Biomass burning is a major source of trace gases and aerosols, influencing atmospheric chemistry and climate. To quantitatively assess its impact, an accurate representation of fire emissions is crucial for the atmospheric modeling community. So far, most studies rely on static emission factors (EF) which convert estimates of dry matter burned to trace gas and aerosol emissions. These EFs are often based on the arithmetic mean of field measurements stratified by biome, neglecting the variability in time and space. Here we present global carbon monoxide (CO) emission estimates from fires based on six EF scenarios with different spatial and temporal variability, using dry matter emission estimates from the Global Fire Emissions Database (GFED). We used the TM5 model to transport these different bottom-up estimates in the atmosphere and found that including spatial and temporal variability in EFs impacted CO mixing ratios substantially. Most scenarios estimated higher CO mixing ratios (up to 40% more CO from fires during the burning season) over boreal regions compared to the GFED standard run, while a decrease (~15%) was estimated over the continent of Africa. A comparison to atmospheric CO observations showed differences of 10–20¿ppb between the scenarios and systematic deviations from local observations. Although temporal correlations of specific EF scenarios improved for certain regions, an overall “best” set of EFs could not be selected. Our results provide a new set of emission estimates that can be used for sensitivity analyses and highlight the importance of better understanding spatial and temporal variability in EFs for atmospheric studies in general and specifically when using CO or aerosols concentration measurements to top-down constrain fire carbon emissions.
    Interannual variability of carbon monoxide emission estimates over South America from 2006 to 2010
    Hooghiemstra, P.B. ; Krol, M.C. ; Leeuwen, T.T. van; Werf, G.R. van der; Novelli, P.C. ; Deeter, M.N. ; Aben, I. ; Rockmann, T. - \ 2012
    Journal of Geophysical Research: Atmospheres 117 (2012). - ISSN 2169-897X
    variational data assimilation - land-use change - climate-change - co emissions - amazon deforestation - brazilian amazon - fire emissions - model tm5 - mopitt - inversion
    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.
    Optimizing global CO emission estimates using a four-dimensional variational data assimilation system and surface network observations
    Hooghiemstra, P.B. ; Krol, M.C. ; Meirink, J.F. ; Bergamaschi, P. ; Werf, G.R. van der; Novelli, P.C. ; Aben, I. ; Röckmann, T. - \ 2011
    Atmospheric Chemistry and Physics 11 (2011)10. - ISSN 1680-7316 - p. 4705 - 4723.
    carbon-monoxide - tropospheric chemistry - fire emissions - model tm5 - inversion - mopitt - adjoint - forest - asia - algorithm
    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.
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