Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region

Potter, Christopher; Klooster, Steven; Carvalho, Claudio; Genovese, Vanessa; Torregrosa, Alicia; Dungan, Jennifer; Bobo, Matthew; Coughlan, Joseph C.

doi:10.1029/2000jd900563

Cited by 87 publications

(53 citation statements)

References 74 publications

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“…1(j-l), (p-r)), with a maximum value during the peak growing season (July-August); while that of NEP occurred during the onset of growing seasons (June). The differentiated timing of the peak IAV revealed a dominant climatic effect on GPP and Re in the growing season (Hollinger et al, 2004), and their offset advanced the maximum IAV of NEP to an earlier date (Potter et al, 2001;Schimel et al, 2001). The DStDev of GPP and Re were almost consistently positive during the whole year.…”

Section: The Annual Dynamics Of Environmental Conditions and Co 2 Fluxesmentioning

confidence: 96%

“…Moreover, such effects are dynamic across mul-tiple spatial and temporal scales (Stoy et al, 2009). These factors include temperature, precipitation, radiation and other climatic factors (Griffis et al, 2000); interrelated physiological and ecological processes (Botta et al, 2000;Griffis et al, 2000); and a dynamic balance between photosynthesis and respiration (Potter et al, 2001;Schimel et al, 2001). According to their sources, the drivers of carbon flux can generally be divided into two categories (Marcolla et al, 2011;Wu et al, 2012): abiotic (climate) and biotic (ecosystem responses).…”

Section: Introductionmentioning

confidence: 99%

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Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands

Zhang

et al. 2016

Agricultural and Forest Meteorology

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a b s t r a c tThe inter-annual variability (IAV) of net ecosystem productivity (NEP) may be caused by both climatic factors and ecosystem responses. In this study, we used eddy covariance (EC) measurements over three typical grasslands in China to investigate the dynamics of NEP and its two components − gross primary productivity (GPP) and ecosystem respiration (Re) and their driving forces. We found that climatic factors and ecosystem response simultaneously influence the IAV of ecosystem carbon fluxes, with a dominant effect arising from an ecosystem response. On a daily scale, carbon fluxes were driven primarily by climatic factors, but effects from an ecosystem response strengthened when the period of analysis was extended. On an annual scale, ecosystem responses weakened the effects of climatic variability on ecosystem carbon fluxes for the three grasslands. This negative feedback demonstrated that ecosystem acclimatization to climate variability can constrain the IAV of carbon fluxes induced by such variability.

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Section: The Annual Dynamics Of Environmental Conditions and Co 2 Fluxesmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands

Zhang

et al. 2016

Agricultural and Forest Meteorology

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“…The Amazon region is of vital importance for the functioning of both regional ecosystems and climate (Fisch et al, 1998;Nobre et al, 2016). Topics for research in recent years have included the relationship between the biosphere and the atmosphere in the Amazon (Fan et al, 1990;Stark et al, 2015); the impacts of land use change (Dickinson and Kennedy, 1992;Fearnside, 2003;Paula et al, 2014;WertzKanounnikoff et al, 2016); and the consequences of urbanization, population growth, and increased anthropogenic emissions to the composition of the atmosphere (Shukla et al, 1990;Potter et al, 2001;Wright, 2005;Malhi et al, 2008;Martin et al, 2016).…”

mentioning

confidence: 99%

“…Compared to temperate urban regions that have been studied in greater detail for ozone production, the tropical region has more intense solar radiation and higher water vapor concentrations (Kuhn et al, 2010). Regional modeling is an important approach for understanding the linked effects (Potter et al, 2001;Isaksen et al, 2009).…”

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confidence: 99%

Power plant fuel switching and air quality in a tropical, forested environment

et al. 2017

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Abstract. How a changing energy matrix for electricity production affects air quality is considered for an urban region in a tropical, forested environment. Manaus, the largest city in the central Amazon Basin of Brazil, is in the process of changing its energy matrix for electricity production from fuel oil and diesel to natural gas over an approximately 10-year period, with a minor contribution by hydropower. Three scenarios of urban air quality, specifically afternoon ozone concentrations, were simulated using the Weather Research and Forecasting (WRF-Chem) model. The first scenario used fuel oil and diesel for electricity production, which was the reality in 2008. The second scenario was based on the fuel mix from 2014, the most current year for which data were available. The third scenario considered nearly complete use of natural gas for electricity production, which is the anticipated future, possibly for 2018. For each case, inventories of anthropogenic emissions were based on electricity generation, refinery operations, and transportation. Transportation and refinery operations were held constant across the three scenarios to focus on effects of power plant fuel switching in a tropical context. The simulated NO x and CO emissions for the urban region decrease by 89 and 55 %, respectively, after the complete change in the energy matrix. The results of the simulations indicate that a change to natural gas significantly decreases maximum afternoon ozone concentrations over the population center, reducing ozone by > 70 % for the most polluted days. The sensitivity of ozone concentrations to the fuel switchover is consistent with a NO xlimited regime, as expected for a tropical forest having high emissions of biogenic volatile organic compounds, high water vapor concentrations, and abundant solar radiation. There are key differences in a shifting energy matrix in a tropical, forested environment compared to other world environments. Policies favoring the burning of natural gas in place of fuel oil and diesel have great potential for ozone reduction and improved air quality for growing urban regions located in tropical, forested environments around the world.

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“…Remote sensing and modeling studies suggest or assume that temperature followed by solar radiation primarily determine variation in NPP in Amazonia, which is especially sensitive to large ENSO events [70,71] although soil depths significantly affect the gross primary production (GPP, the sum of net primary production (NPP) and autotrophic respiration) [68,72]. While temperature appears to exert a strong influence on NPP in some models [64], other models suggest that radiation increases carbon fluxes in Amazonian [65,73].…”

Section: Forest Productivitymentioning

confidence: 99%

Biome-Scale Forest Properties in Amazonia Based on Field and Satellite Observations

Anderson

2012

Remote Sensing

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Amazonian forests are extremely heterogeneous at different spatial scales. This review intends to present the large-scale patterns of the ecosystem properties of Amazonia, and focuses on two parts of the main components of the net primary production: the long-lived carbon pools (wood) and short-lived pools (leaves). First, the focus is on forest biophysical properties, and secondly, on the macro-scale leaf phenological patterns of these forests, looking at field measurements and bringing into discussion the recent findings derived from remote sensing dataset. Finally, I discuss the results of the three major droughts that hit Amazonia in the last 15 years. The panorama that emerges from this review suggests that slow growing forests in central and eastern Amazonia, where soils are poorer, have significantly higher above ground biomass and higher wood density, trees are higher and present lower proportions of large-leaved species than stands in northwest and southwest Amazonia. However, the opposite pattern is observed in relation to forest productivity and dynamism, which is higher in western Amazonia than in central and eastern forests. The spatial patterns on leaf phenology across Amazonia are less marked. Field data from different forest formations showed that new leaf production can be unrelated to climate seasonality, timed with radiation, timed with rainfall and/or river levels. Oppositely, satellite images exhibited a large-scale synchronized peak in new leaf production during the dry season. Satellite data and field measurements bring contrasting results for the 2005 drought. Discussions on data processing and filtering, aerosols effects and a combined analysis with field and satellite images are presented. It is suggested that to improve the understanding of the large-scale patterns on Amazonian forests, integrative analyses that combine new technologies in remote sensing and long-term field ecological data are imperative.

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Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region

Cited by 87 publications

References 74 publications

Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands

Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands

Power plant fuel switching and air quality in a tropical, forested environment

Biome-Scale Forest Properties in Amazonia Based on Field and Satellite Observations

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