We analyze the North American budget for carbon monoxide using data for CO and formaldehyde concentrations from tall towers and aircraft in a model-data assimilation framework. The Stochastic Time-Inverted Lagrangian Transport model for CO (STILT-CO) determines local to regional-scale CO contributions associated with production from fossil fuel combustion, biomass burning, and oxidation of volatile organic compounds (VOCs) using an ensemble of Lagrangian particles driven by high resolution assimilated meteorology. In many cases, the model demonstrates high fidelity simulations of hourly surface data from tall towers and point measurements from aircraft, with somewhat less satisfactory performance in coastal regions and when CO from large biomass fires in Alaska and the Yukon Territory influence the continental US.

Inversions of STILT-CO simulations for CO and formaldehyde show that current inventories of CO emissions from fossil fuel combustion are significantly too high, by almost a factor of three in summer and a factor two in early spring, consistent with recent analyses of data from the INTEX-A aircraft program. Formaldehyde data help to show that sources of CO from oxidation of CH₄ and other VOCs represent the dominant sources of CO over North America in summer.

Amazon forests are potentially globally significant sources or sinks for atmospheric carbon dioxide. In this study, we characterize the spatial trends in carbon storage and fluxes in both live and dead biomass (necromass) in two Amazonian forests, the Biological Dynamic of Forest Fragments Project (BDFFP), near Manaus, Amazonas, and the Tapajós National Forest (TNF) near Santarém, Pará. We assessed coarse woody debris (CWD) stocks, tree growth, mortality, and recruitment in ground-based plots distributed across the terra firme forest at both sites. Carbon dynamics were similar within each site, but differed significantly between the sites. The BDFFP and the TNF held comparable live biomass (167 ± 7.6 MgC·ha⁻¹ versus 149 ± 6.0 MgC·ha⁻¹, respectively), but stocks of CWD were 2.5 times larger at TNF (16.2 ± 1.5 MgC·ha⁻¹ at BDFFP, versus 40.1 ± 3.9 MgC·ha⁻¹ at TNF). A model of current forest dynamics suggests that the BDFFP was close to carbon balance, and its size class structure approximated a steady state. The TNF, by contrast, showed rapid carbon accrual to live biomass (3.24 ± 0.22 MgC·ha⁻¹·a⁻¹ in TNF, 2.59 ± 0.16 MgC·ha⁻¹·a⁻¹ in BDFFP), which was more than offset by losses from large stocks of CWD, as well as ongoing shifts of biomass among size classes. This pattern in the TNF suggests recovery from a significant disturbance. The net loss of carbon from the TNF will likely last 10–15 years after the initial disturbance (controlled by the rate of decay of coarse woody debris), followed by uptake of carbon as the forest size class structure and composition continue to shift. The frequency and longevity of forests showing such disequilibruim dynamics within the larger matrix of the Amazon remains an essential question to understanding Amazonian carbon balance.

Eddy covariance flux towers provide continuous measurements of net ecosystem carbon exchange (NEE) for a wide range of climate and biome types. However, these measurements only represent the carbon fluxes at the scale of the tower footprint. To quantify the net exchange of carbon dioxide between the terrestrial biosphere and the atmosphere for regions or continents, flux tower measurements need to be extrapolated to these large areas. Here we used remotely sensed data from the Moderate Resolution Imaging Spectrometer (MODIS) instrument on board the National Aeronautics and Space Administration?s (NASA) Terra satellite to scale up AmeriFlux NEE measurements to the continental scale.We first combined MODIS and AmeriFlux data for representative U.S. ecosystems to develop a predictive NEE model using a modified regression tree approach. The predictive model was trained and validated using eddy flux NEE data over the periods 2000?2004 and 2005?2006, respectively. We found that the model predicted NEE well (r = 0.73, p < 0.001). We then applied the model to the continental scale and estimated NEE for each 1 km 1 km cell across the conterminous U.S. for each 8-day interval in 2005 using spatially explicit MODIS data. The model generally captured the expected spatial and seasonal patterns of NEE as determined from measurements and the literature. Our study demonstrated that our empirical approach is effective for scaling up eddy flux NEE measurements to the continental scale and producing wall-to-wall NEE estimates across multiple biomes. Our estimates may provide an independent dataset from simulations with biogeochemical models and inverse modeling approaches for examining the spatiotemporal patterns of NEE and constraining terrestrial carbon budgets over large areas.

We present top-down emission constraints for two non-CO₂ greenhouse gases in large areas of the U.S. and southern Canada during early summer. Collocated airborne measurements of methane and nitrous oxide acquired during the COBRA-NA campaign in May–June 2003, analyzed using a receptor-oriented Lagrangian particle dispersion model, provide robust validation of independent bottom-up emission estimates from the EDGAR and GEIA inventories. We find that the EDGAR CH₄ emission rates are slightly low by a factor of 1.08 ± 0.15 (2σ), while both EDGAR and GEIA N₂O emissions are significantly too low, by factors of 2.62 ± 0.50 and 3.05 ± 0.61, respectively, for this region. Potential footprint bias may expand the statistically retrieved uncertainties. Seasonality of agricultural N₂O emissions may help explain the discrepancy. Total anthropogenic U.S. and Canadian emissions would be 49 Tg CH₄ and 4.3 Tg N₂O annually, if these inventory scaling factors applied to all of North America.

Process-based models are effective tools to synthesize and/or extrapolate measured carbon (C) exchanges from individual sites to large scales. In this study, we used a C- and nitrogen (N)-cycle coupled ecosystem model named CN-CLASS (Carbon Nitrogen-Canadian Land Surface Scheme) to study the role of primary climatic controls and site-specific C stocks on the net ecosystem productivity (NEP) of seven intermediate-aged to mature coniferous forest sites across an east–west continental transect in Canada. The model was parameterized using a common set of parameters, except for two used in empirical canopy conductance–assimilation, and leaf area–sapwood relationships, and then validated using observed eddy covariance flux data. Leaf Rubisco-N dynamics that are associated with soil–plant N cycling, and depend on canopy temperature, enabled the model to simulate site-specific gross ecosystem productivity (GEP) reasonably well for all seven sites. Overall GEP simulations had relatively smaller differences compared with observations vs. ecosystem respiration (RE), which was the sum of many plant and soil components with larger variability and/or uncertainty associated with them. Both observed and simulated data showed that, on an annual basis, boreal forest sites were either carbon-neutral or a weak C sink, ranging from 30 to 180 g C m⁻² yr⁻¹; while temperate forests were either a medium or strong C sink, ranging from 150 to 500 g C m⁻² yr⁻¹, depending on forest age and climatic regime. Model sensitivity tests illustrated that air temperature, among climate variables, and aboveground biomass, among major C stocks, were dominant factors impacting annual NEP. Vegetation biomass effects on annual GEP, RE and NEP showed similar patterns of variability at four boreal and three temperate forests. Air temperature showed different impacts on GEP and RE, and the response varied considerably from site to site. Higher solar radiation enhanced GEP, while precipitation differences had a minor effect. Magnitude of forest litter content and soil organic matter (SOM) affected RE. SOM also affected GEP, but only at low levels of SOM, because of low N mineralization that limited soil nutrient (N) availability. The results of this study will help to evaluate the impact of future climatic changes and/or forest C stock variations on C uptake and loss in forest ecosystems growing in diverse environments.

Horizontal and vertical CO₂ fluxes and gradients were made in an Amazon tropical rain forest, the Tapajós National Forest Reserve (FLONA-Tapajós: 54°58'W, 2°51'S). Two observational campaigns in 2003 and 2004 were conducted to describe subcanopy flows, clarify their relationship to winds above the forest, and estimate how they may transport CO₂ horizontally. It is now recognized that subcanopy transport of respired CO₂ is missed by budgets that rely only on single point eddy covariance measurements, with the error being most important under nocturnal calm conditions. We tested the hypothesis that horizontal mean transport, not previously measured in tropical forests, may account for the missing CO₂ in such conditions. A subcanopy network of wind and CO₂ sensors was installed. Significant horizontal transport of CO₂ was observed in the lowest 10 m of the canopy. Results indicate that CO₂ advection accounted for 73% and 71%, respectively, of the carbon budget for all calm nights evaluated during dry and wet periods. We found that horizontal advection is likely important to the canopy CO₂ budget even for conditions with the above-canopy friction velocity higher than commonly used thresholds.

We present the Vegetation Photosynthesis and Respiration Model (VPRM), a satellite-based assimilation scheme that estimates hourly values of Net Ecosystem Exchange (NEE) of CO₂ for 12 North American biomes using the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI), derived from reflectance data of the Moderate Resolution Imaging Spectroradiometer (MODIS), plus high-resolution data for sunlight and air temperature. The motivation is to provide reliable, fine-grained first-guess fields of surface CO₂ fluxes for application in inverse models at continental and smaller scales. An extremely simple mathematical structure, with minimal numbers of parameters, facilitates optimization using in situ data, with finesse provided by maximal infusion of observed NEE and environmental data from networks of eddy covariance towers across North America (AmeriFlux and Fluxnet Canada). Cross validation showed that the VPRM has strong prediction ability for hourly to monthly timescales for sites with similar vegetation. The VPRM also provides consistent partitioning of NEE into Gross Ecosystem Exchange (GEE, the light-dependent part of NEE) and ecosystem respiration (R, the light-independent part), half-saturation irradiance of ecosystem photosynthesis, and annual sum of NEE at all eddy flux sites for which it is optimized. The capability to provide reliable patterns of surface flux for fine-scale inversions is presently limited by the number of vegetation classes for which NEE can be constrained by the current network of eddy flux sites and by the accuracy of MODIS data and data for sunlight.

The controls on uptake and release of CO ₂ by tropical rainforests, and the responses to a changing climate, are major uncertainties in global climate change models. Eddy-covariance measurements potentially provide detailed data on CO ₂ exchange and responses to the environment in these forests, but accurate estimates of the net ecosystem exchange of CO ₂ (NEE) and ecosystem respiration (R _eco ) require careful analysis of data representativity, treatment of data gaps, and correction for systematic errors. This study uses the comprehensive data from our study site in an old-growth tropical rainforest near Santarem, Brazil, to examine the biases in NEE and R _eco potentially associated with the two most important sources of systematic error in Eddy-covariance data: lost nighttime flux and missing canopy storage measurements. We present multiple estimates for the net carbon balance and R _eco at our site, including the conventional “u* filter”, a detailed bottom-up budget for respiration, estimates by similarity with ²²² Rn, and an independent estimate of respiration by extrapolation of daytime Eddy flux data to zero light. Eddy-covariance measurements between 2002 and 2006 showed a mean net ecosystem carbon loss of 0.25±0.04μmolm ⁻² s ⁻¹ , with a mean respiration rate of 8.60±0.11μmolm ⁻² s ⁻¹ at our site. We found that lost nocturnal flux can potentially introduce significant bias into these results. We develop robust approaches to correct for these biases, showing that, where appropriate, a site-specific u* threshold can be used to avoid systematic bias in estimates of carbon exchange. Because of the presence of gaps in the data and the day–night asymmetry between storage and turbulence, inclusion of canopy storage is essential to accurate assessments of NEE. We found that short-term measurements of storage may be adequate to accurately model storage for use in obtaining ecosystem carbon balance, at sites where storage is not routinely measured. The analytical framework utilized in this study can be applied to other Eddy-covariance sites to help correct and validate measurements of the carbon cycle and its components.

The global emissions of isoprene are calculated at 0.5° resolution for each year between 1995 and 2006, based on the MEGAN (Model of Emissions of Gases and Aerosols from Nature) version 2 model (Guenther et al., 2006) and a detailed multi-layer canopy environment model for the calculation of leaf temperature and visible radiation fluxes. The calculation is driven by meteorological fields – air temperature, cloud cover, downward solar irradiance, windspeed, volumetric soil moisture in 4 soil layers – provided by analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The estimated annual global isoprene emission ranges between 374 Tg (in 1996) and 449 Tg (in 1998 and 2005), for an average of ca. 410 Tg/year over the whole period, i.e. about 30% less than the standard MEGAN estimate (Guenther et al., 2006). This difference is due, to a large extent, to the impact of the soil moisture stress factor, which is found here to decrease the global emissions by more than 20%. In qualitative agreement with past studies, high annual emissions are found to be generally associated with El Niño events. The emission inventory is evaluated against flux measurement campaigns at Harvard forest (Massachussets) and Tapajós in Amazonia, showing that the model can capture quite well the short-term variability of emissions, but that it fails to reproduce the observed seasonal variation at the tropical rainforest site, with largely overestimated wet season fluxes. The comparison of the HCHO vertical columns calculated by a chemistry and transport model (CTM) with HCHO distributions retrieved from space provides useful insights on tropical isoprene emissions. For example, the relatively low emissions calculated over Western Amazonia (compared to the corresponding estimates in the inventory of Guenther et al., 1995) are validated by the excellent agreement found between the CTM and HCHO data over this region. The parameterized impact of the soil moisture stress on isoprene emissions is found to reduce the model/data bias over Australia, but it leads to underestimated emissions near the end of the dry season over subtropical Africa.

Historically, northern peatlands have functioned as a carbon sink, sequestering large amounts of soil organic carbon, mainly due to low decomposition in cold, largely waterlogged soils. The water table, an essential determinant of soil-organic-carbon dynamics interacts with soil organic carbon. Because of the high water-holding capacity of peat and its low hydraulic conductivity, accumulation of soil organic carbon raises the water table, which lowers decomposition rates of soil organic carbon in a positive feedback loop. This two-way interaction between hydrology and biogeochemistry has been noted but is not reproduced in process-based simulations. Here we present simulations with a coupled physical–biogeochemical soil model with peat depths that are continuously updated from the dynamic balance of soil organic carbon. Our model reproduces dynamics of shallow and deep peatlands in northern Manitoba, Canada, on both short and longer timescales. We find that the feedback between the water table and peat depth increases the sensitivity of peat decomposition to temperature, and intensifies the loss of soil organic carbon in a changing climate. In our long-term simulation, an experimental warming of 4 °C causes a 40% loss of soil organic carbon from the shallow peat and 86% from the deep peat. We conclude that peatlands will quickly respond to the expected warming in this century by losing labile soil organic carbon during dry periods. Earth and Planetary Sciences Organismic and Evolutionary Biology Version of Record

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