Estimates of global biomass burning emissions for reactive greenhouse gases (CO, NMHCs, and NOx) and CO2

Jain, Atul K.; Tao, Zhining; Yang, Xiaojuan; Gillespie, Conor S

doi:10.1029/2005jd006237

Cited by 64 publications

(53 citation statements)

References 89 publications

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“…Nepstad et al, 1999;Asner et al, 2005) and open vegetation burning (e.g. van der Werd et al, 2004;Jain et al, 2006;Ito et al, 2007b). This may imply that accurate estimates of the shortterm flux would play a key role in closing the gap between the bottom-up and top-down estimates.…”

Section: Usamentioning

confidence: 99%

Can we reconcile differences in estimates of carbon fluxes from land-use change and forestry for the 1990s?

et al. 2008

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Abstract. The effect of Land Use Change and Forestry (LUCF) on terrestrial carbon fluxes can be regarded as a carbon credit or debit under the UNFCCC, but scientific uncertainty in the estimates for LUCF remains large. Here, we assess the LUCF estimates by examining a variety of models of different types with different land cover change maps in the 1990s. Annual carbon pools and their changes are separated into different components for separate geographical regions, while annual land cover change areas and carbon fluxes are disaggregated into different LUCF activities and the biospheric response due to CO 2 fertilization and climate change. We developed a consolidated estimate of the terrestrial carbon fluxes that combines book-keeping models with process-based biogeochemical models and inventory estimates and yields an estimate of the global terrestrial carbon flux that is within the uncertainty range developed in the IPCC 4th Assessment Report. We examined the USA and Brazil as case studies in order to assess the cause of differences from the UNFCCC reported carbon fluxes. Major differences in the litter and soil organic matter components are found for the USA. Differences in Brazil result from assumptions about the LUC for agricultural purposes. The effects of CO 2 fertilization and climate change also vary significantly in Brazil. Our consolidated estimate shows that the small Correspondence to: A. Ito (akinorii@jamstec.go.jp) sink in Latin America is within the uncertainty range from inverse models, but that the sink in the USA is significantly smaller than the inverse models estimates. Because there are different sources of errors at the country level, there is no easy reconciliation of different estimates of carbon fluxes at the global level. Clearly, further work is required to develop data sets for historical land cover change areas and models of biogeochemical changes for an accurate representation of carbon uptake or emissions due to LUC.

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Section: Usamentioning

confidence: 99%

Can we reconcile differences in estimates of carbon fluxes from land-use change and forestry for the 1990s?

et al. 2008

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“…The most common approach to derive fire emissions is based on the use of information on the burned area (e.g. Seiler and Crutzen, 1980;Crutzen and Andreae, 1990;Andreae and Merlet, 2001;Hao et al, 1996;van der Werf et al, , 2010Jain et al, 2006). Apart from the burned area estimates which, in recent years, have become available from satellite measurements (e.g.…”

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confidence: 99%

Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region

et al. 2011

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Abstract. Numerous wildfires provoked by an unprecedented intensive heat wave caused continuous episodes of extreme air pollution in several Russian cities and densely populated regions, including the Moscow region. This paper analyzes the evolution of the surface concentrations of CO, PM 10 and ozone over the Moscow region during the 2010 heat wave by integrating available ground based and satellite measurements with results of a mesoscale model. The CHIMERE chemistry transport model is used and modified to include the wildfire emissions of primary pollutants and the shielding effect of smoke aerosols on photolysis. The wildfire emissions are derived from satellite measurements of the fire radiative power and are optimized by assimilating data of ground measurements of carbon monoxide (CO) and particulate matter (PM 10 ) into the model. It is demonstrated that the optimized simulations reproduce independent observations, which were withheld during the optimisation procedure, quite adequately (specifically, the correlation coefficient of daily time series of CO and PM 10 exceeds 0.8) and that inclusion of the fire emissions into the model significantly improves its performance. The model results show that wildfires are the principal factor causing the observed air pollution episode associated with the extremely high levels of daily mean CO and PM 10 concentrations (up to 10 mg m −3 and 700 µg m −3 in the averages over available monitoring sites, respectively), although accumulation of anthropogenic pollution was also favoured by a stagnant meteorological sitCorrespondence to: I. B. Konovalov (konov@appl.sci-nnov.ru) uation. Indeed, ozone concentrations were simulated to be episodically very large (>400 µg m −3 ) even when fire emissions were omitted in the model. It was found that fire emissions increased ozone production by providing precursors for ozone formation (mainly VOC), but also inhibited the photochemistry by absorbing and scattering solar radiation. In contrast, diagnostic model runs indicate that ozone concentrations could reach very high values even without fire emissions which provide "fuel" for ozone formation, but, at the same time, inhibit it as a result of absorption and scattering of solar radiation by smoke aerosols. A comparison of MOPITT CO measurements and corresponding simulations indicates that the observed episodes of extreme air pollution in Moscow were only a part of a very strong perturbation of the atmospheric composition, caused by wildfires, over European Russia. It is estimated that 2010 fires in this region emitted ∼10 Tg CO, thus more than 85 % of the total annual anthropogenic CO emissions. About 30 % of total CO fire emissions in European Russia are identified as emissions from peat fires.

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“…ISAM is applied to examine the impacts of changing CO 2 in the atmosphere, fire, and land use change on terrestrial ecosystems functions [12], [13], [14]. ISAM has also been used to describe the dynamic of the terrestrial biosphere carbon and nitrogen [11], [10] and was a part of all five IPCC Working Group I assessment reports on science of climate change.…”

Section: Background a Isam: Integrated Science Assessment Modelmentioning

confidence: 99%

Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer

Miller

Robson

Masri

et al. 2014

2014 43rd International Conference on Parallel Processing

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Abstract-We present the progression of developments necessary to scale the ISAM land surface model from single nodes and small clusters with unusually large per-node memory to much larger systems with more common configurations. These efforts include load balancing, conventional library-based output parallelization to reduce memory load, and parallel-in-time data input. On Hopper, a Cray XE6 machine, the result was strong scaling from 256 cores to 16k cores with an efficiency of 32.9%. On Edison, a Cray XC30 machine, the code strong scales from 256 cores to 16k cores with an efficiency of 51.4%. These largescale gains, and the associated performance increases at smaller scale, enable greater scientific productivity for the users of ISAM and open the possibilities of increased resolution in time and space and greater physical fidelity for the simulated processes while remaining computationally feasible.

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Estimates of global biomass burning emissions for reactive greenhouse gases (CO, NMHCs, and NO_x) and CO₂

Cited by 64 publications

References 89 publications

Can we reconcile differences in estimates of carbon fluxes from land-use change and forestry for the 1990s?

Can we reconcile differences in estimates of carbon fluxes from land-use change and forestry for the 1990s?

Atmospheric impacts of the 2010 Russian wildfires: integrating modelling and measurements of an extreme air pollution episode in the Moscow region

Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer

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