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The discrepancy between simulated and observed globally averaged monthly atmospheric concentrations of carbon dioxide could be attributed either to deficiencies in the observation network or to inadequacies in the global carbon cycle models. This paper shows that model results could be brought closer to observations by improving model components that describe the seasonal changes in the storage of quickly decaying fractions of litter. [ABSTRACT FROM AUTHOR]

The long-term and large scale dynamics of ecosystems are in large part determined by the performances of individual plants in competition with one another for light, water and nutrients. Woody biomass, a pool of carbon (C) larger than 50% of atmospheric CO2, exists because of height-structured competition for light. However, most of the current Earth System Models that predict climate change and C cycle feedbacks lack both a mechanistic formulation for height-structured competition for light and an explicit scaling from individual plants to the globe. In this study, we incorporate height- structured competition and explicit scaling from individuals to ecosystems into the land model (LM3) currently used in the Earth System Models developed by the Geophysical Fluid Dynamics Laboratory (GFDL). The height-structured formulation is based on the Perfect Plasticity Approximation (PPA), which has been shown to accurately scale from individual-level plant competition for light, water and nutrients to the dynamics of whole communities. Because of the tractability of the PPA, the coupled LM3-PPA model is able to include a large number of phenomena across a range of spatial and temporal scales, and still retain computational tractability, as well as close linkages to mathematically tractable forms of the model. We test a range of predictions against data from temperate broadleaved forests in the northern USA. The results show the model predictions agree with diurnal and annual C fluxes, growth rates of individual trees in the canopy and understory, tree size distributions, and species-level population dynamics during succession. We also show how the competitively optimal allocation strategy - the strategy that can competitively exclude all others - shifts as a function of the atmospheric CO2 concentration. This strategy is referred as an evolutionary stable strategy (ESS) in the ecological literature and is typically not the same as a productivity- or growth-maximizing strategy. Model simulations predict that C sinks caused by CO2 fertilization in forests limited by light and water will be down-regulated if allocation tracks changes in the competitive optimum. The implementation of the model in this paper is for temperate broadleaved forest trees, but the formulation of the model is general. It can be expanded to include other growth forms and physiologies simply by altering parameter values. [ABSTRACT FROM AUTHOR]

Isotope ratio infrared spectroscopy (IRIS) provides an in-situ technique for measuring δ13C in atmospheric CO2. A number of methods have been proposed for calibrating the IRIS measurements, but few studies have systematically evaluated their accuracy for atmospheric applications. In this study, we carried out laboratory and ambient measurements with two commercial IRIS analyzers and compared the accuracy of four calibration strategies. We found that calibration based on the 12C and 13C mixing ratios (Bowling et al., 2003) and that based on linear interpolation of the measured delta using the mixing ratio of the major isotopologue (Lee et al., 2005) yielded accuracy better than 0.06‰. Over a 7-day atmospheric measurement in Beijing, the two analyzers differed by 9.44±1.65‰ (mean ±1 standard deviation of hourly values) before calibration and agreed to within -0.02±0.18‰ after properly calibration. However, even after calibration the difference between the two analyzers showed a slight correlation with concentration, and this concentration dependence propagated through the Keeling analysis resulting in a much larger difference of 2.44‰ for the Keeling intercept. The high sensitivity of the Keeling analysis to the concentration dependence underscores the challenge of IRIS for atmospheric research. [ABSTRACT FROM AUTHOR]

We intercompare results of three independent approaches to quantify a vented CO2 release at a strongly non-uniform CO2 Earth degassing at Caldara di Manziana, central Italy. An integrated path differential absorption lidar prototype and a commercial open path FTIR system were measuring column averaged CO2 concentrations in parallel at two different paths. An Eulerian gas dispersion model simulated 3-D CO2 concentration maps in the same area, using in situ CO2 flux input data acquired at 152 different points. Local processes the model does not account for, such as small-scale and shortlived wind eddies, govern CO2 concentrations in the instrument measurement paths. The model, on the other hand, also considers atmospheric effects that are out of the field of view of the instruments. Despite this we find satisfactory agreement between modeled and measured CO2 concentrations under certain meteorological conditions. Under these conditions the results suggest that an Eulerian dispersion model and optical remote sensing can be used as an integrated, complementary monitoring approach for CO2 hazard or leakage assessment. Furthermore, the modeling may assist in evaluating CO2 sensing surveys in the future. CO2 column amounts from differential absorption lidar are in line with those from FTIR for both paths with a mean residual of the time series of 44 and 34 ppm, respectively. This experiment is a fundamental step forward in the deployment of the differential absorption lidar prototype as a highly portable active remote sensing instrument probing vented CO2 emissions, including volcanoes. [ABSTRACT FROM AUTHOR]

CO2 concentration gradients inside soil aggregates, caused by the respiration of soil microorganisms and fungal hyphae, might lead to variations in the soil solution chemistry on a mm-scale, and to an underestimation of the CO2 storage. But, up to now, there seems to be no feasible method for measuring CO2 inside natural aggregates with sufficient spatial resolution. We combined a one-dimensional model for gas diffusion in the inter-aggregate pore-space with a cylinder diffusion model, simulating the consumption/production and diffusion of O2 and CO2 inside soil aggregates with airand water-filled pores. Our model predicts that for aerobic respiration (respiratory quotient= 1) the intra-aggregate increase in the CO2 partial pressure can never be higher than 0.9 kPa for siliceous, and 0.08 kPa for calcaric aggregates, independent of the level of water-saturation. This suggests that only for siliceous aggregates CO2 produced by aerobic respiration might cause a high small-scale spatial variability in the soil solution chemistry. In calcaric aggregates, however, the contribution of carbonate species to the CO2 transport should lead to secondary carbonates on the aggregate surfaces. As regards the total CO2 storage in aerobic soils, both siliceous and calcaric, the effect of intra-aggregate CO2 gradients seems to be negligible. To assess the effect of anaerobic respiration on the intra-aggregate CO2 gradients, the development of a device for measuring CO2 on a mm-scale in soils is indispensable. [ABSTRACT FROM AUTHOR]

The article presents a study which discusses the application of a Eulerian-Lagrangian model to simulate atmospheric carbon dioxide transport simulations. It relates that the said model is a combination of a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model. The study reveals the potential of the Eulerian-Lagrangian model to simulate high-frequency atmospheric carbon dioxide concentrations at many locations worldwide.

The article discusses a research study which examines the use of Eulerian-Lagrangian transport model for the simulation of atmospheric carbon dioxide variability. The study aimed to demonstrate the advantages of using a coupled atmospheric-tracer transport model for the reproduction of tracer gas variation. The correlation coefficients and variance ratios between the simulated deseasonalized model and observed carbon dioxide concentrations are calculated.

The net ecosystem exchange of CO2 (NEE) between the atmosphere and a beech forest (Sorø, Denmark) showed significant interannual variation (IAV) over 13 years (1997-2009) of observations. The forest sequestered, on average, 157 g C m-2 yr-1, ranging from a source of 32 to a sink of 344 g C m-2 yr-1 in 1998 and 2008, respectively. The objectives of this study were to evaluate to what extent and at which temporal scale, climatic variability (through direct response) and changes in ecosystem functional properties (through biotic response) regulated the IAV in the ecosystem carbon balance. To address this question, we performed correlation analysis between the carbon fluxes and climate variables at different time scales. The response of CO2 exchange to climatic variability was significantly higher at short time scales and the limiting factors changed intra-annually. Combinations of climate anomalies in different periods of the year either intensified or attenuated the aggregated ecosystem responses, implying that the changing distribution of climate anomalies, in addition to the average climate change, could have stronger impacts on the ecosystem carbon balance in the future. A semi empirical model was used to estimate a set of parameter time series for each of the 13 years, which was considered to represent the functional properties of the ecosystem. The climate and parameter time series were applied factorially by year to quantify their relative importance for the IAV in carbon flux. At an annual time scale, as much as 77% of the IAV in NEE could be attributed to the variation in both photosynthesis and respiration related model parameters, indicating a strong influence of functional change. The possible causes for the observed functional change could not be addressed with the available dataset. This demonstrates the need for more targeted experiments, such as long-term measurements of leaf nitrogen content. Our approach incorporated seasonal variation in the ecosystem status and demonstrated a significant role of biotic factors on the carbon dynamics in a typical temperate deciduous forest. The method can be applied at other sites to explore ecosystem behaviour across different plant functional types and climate gradients. Further, this approach showed how important it is to incorporate functional change in process based models, which could guide model development and consequently reduce the uncertainties in long-term projection of global ecosystem carbon balance. [ABSTRACT FROM AUTHOR]

Solar radiation management (SRM) has been proposed as a form of geoengineering to reduce the climate effects of anthropogenic greenhouse gas emissions. Modeling studies have concluded that SRM, through a reduction in total solar irradiance by approximately 2%, roughly compensates for global mean temperature changes from a doubling of carbon dioxide concentrations. This paper examines the impact of SRM on the terrestrial hydrologic cycle using the Community Land Model, version 4, coupled to the Community Atmosphere Model, version 4, with reductions in solar radiation relative to simulations with present-day and elevated CO2 concentrations. There are significant global and regional impacts due to vegetation-climate interactions that are not compensated when reductions in total solar irradiance of 1%, 2%, and 3% are imposed on top of a doubling of present-day CO2 concentrations. Water cycling slows down under SRM, including decreases in global mean precipitation and evapotranspiration. Changes in runoff and soil moisture are spatially and temporally variable, with implications for local water availability. In the tropics, evapotranspiration decreases because of increases in vegetation water use efficiency. In northern midlatitudes, soil moisture increases when evapotranspiration decreases, with some exceptions during boreal summer. Changes in soil evaporation influence water cycling in the southern subtropics, rather than changes in transpiration. The hydrologic response to SRM is nonlinear, with global mean decreases greater than expected. These results imply that SRM may not compensate for higher greenhouse gas concentrations when one considers land-atmosphere interactions. [ABSTRACT FROM AUTHOR]

Changes in Earth's temperature have significant impacts on the global carbon cycle that vary at different time scales, yet to quantify such impacts with a simple scheme is traditionally deemed difficult. Here, we show that, by incorporating a temperature sensitivity parameter (1.64 ppm yr °C) into a simple linear carbon-cycle model, we can accurately characterize the dynamic responses of atmospheric carbon dioxide (CO) concentration to anthropogenic carbon emissions and global temperature changes between 1850 and 2010 ( r > 0.96 and the root-mean-square error < 1 ppm for the period from 1960 onward). Analytical analysis also indicates that the multiplication of the parameter with the response time of the atmospheric carbon reservoir (~12 year) approximates the long-term temperature sensitivity of global atmospheric CO concentration (~15 ppm °C), generally consistent with previous estimates based on reconstructed CO and climate records over the Little Ice Age. Our results suggest that recent increases in global surface temperatures, which accelerate the release of carbon from the surface reservoirs into the atmosphere, have partially offset surface carbon uptakes enhanced by the elevated atmospheric CO concentration and slowed the net rate of atmospheric CO sequestration by global land and oceans by ~30% since the 1960s. The linear modeling framework outlined in this paper thus provides a useful tool to diagnose the observed atmospheric CO dynamics and monitor their future changes. [ABSTRACT FROM AUTHOR]

The spatial and temporal gradients in atmospheric CO2 contain signatures of carbon fluxes, and as part of inverse studies, these signatures have been combined with atmospheric models to infer carbon sources and sinks. However, such studies have yet to yield finer-scale, regional fluxes over the continent that can be linked to ecosystem processes and ground-based observations. The reasons for this gap are twofold: lack of atmospheric observations over the continent and model deficiencies in interpreting such observations. This paper describes a series of intensive atmospheric sampling field programmes designed as pilot experiments to bridge the observational gap over the continent and to help test and develop models to interpret these observations. We summarize recent results emerging from this work, outlining the role of the intensive atmospheric programmes in collecting CO2 data in both the vertical and horizontal dimensions. These data: (1) quantitatively establish the spatial variability of CO2 and the associated errors from neglecting this variability in models; (2) directly measure regional carbon fluxes from airmass-following experiments and (3) challenge models to reduce and account for uncertainties in atmospheric transport. We conclude with a look towards the future, outlining ways in which intensive atmospheric sampling can contribute towards advancing carbon science. [ABSTRACT FROM AUTHOR]

This paper discusses the interplay between the choice of the discount rate, greenhouse gas mitigation and endogenous technological change. Neglecting the issue of uncertainty it is shown that the Green Golden Rule stock of atmospheric carbon is uniquely determined, but is not affected by technological change. More generally it is shown analytically within the framework of a reduced model of integrated assessment that the optimal stationary stocks of atmospheric carbon depend on the choice of the discount rate, but are independent of the stock of technological knowledge. These results are then reinforced numerically in a fully specified integrated assessment analysis. [Copyright &y& Elsevier]

This paper presents a socio-economically disaggregated framework for attributing CO2 emissions to people's high level functional needs. Based around a quasi-multi-regional input–output (QMRIO) model, the study, in theory, takes into account all CO2 emissions that arise from energy used in production of goods and services to satisfy UK household demand, whether the emissions occur in the UK or abroad. Results show that CO2 emissions attributable to households were 15% above 1990 levels in 2004, and that although absolute decoupling occurred between household expenditure and CO2 during the UK's switch from coal to gas in the early 1990s, since then only slight relative decoupling is evident. The proportion of CO2 that arises outside UK borders in support of UK consumption is rising, and reducing these emissions is particularly problematic in a global trading system. Investigation into the carbon footprint of different segments of the UK population shows wide variation: the segment with the highest carbon footprint emits 64% more CO2 than the segment with the lowest. Results show that recreation and leisure are responsible for over one quarter of CO2 emissions in a typical UK household in 2004. We conclude that expanding lifestyle aspirations are significant factors in driving household CO2 emissions, but the study also emphasizes that attention must be paid to the infrastructures and institutions that result in considerable amounts of CO2 being locked up in basic household activities through which people meet their everyday needs for subsistence, protection, and communication with family and friends. The findings highlight the sheer scale of the challenge facing UK policy-makers, and suggest that policies should be targeted towards segments of society responsible for the highest carbon footprints. [Copyright &y& Elsevier]

The spatial and temporal gradients in atmospheric CO2 contain signatures of carbon fluxes, and as part of inverse studies, these signatures have been combined with atmospheric models to infer carbon sources and sinks. However, such studies have yet to yield finer-scale, regional fluxes over the continent that can be linked to ecosystem processes and ground-based observations. The reasons for this gap are twofold: lack of atmospheric observations over the continent and model deficiencies in interpreting such observations. This paper describes a series of intensive atmospheric sampling field programmes designed as pilot experiments to bridge the observational gap over the continent and to help test and develop models to interpret these observations. We summarize recent results emerging from this work, outlining the role of the intensive atmospheric programmes in collecting CO2 data in both the vertical and horizontal dimensions. These data: (1) quantitatively establish the spatial variability of CO2 and the associated errors from neglecting this variability in models; (2) directly measure regional carbon fluxes from airmass-following experiments and (3) challenge models to reduce and account for uncertainties in atmospheric transport. We conclude with a look towards the future, outlining ways in which intensive atmospheric sampling can contribute towards advancing carbon science. [ABSTRACT FROM AUTHOR]

Previous studies have reported that human influences are required to explain the observed global warming using a linear model (LM) ΔT=λLMΔF that relates change in solar forcing ΔF to change in global mean temperature (GMT) ΔT. This model has the shortcoming of assuming a given ΔF causes the same ΔT irrespective of the value of the initial global warming rate (dT/dy)i. Analysis of the GMT data showed that this warming rate has been increasing linearly since steady state ((dT/dy)o=0 for year yo=1864.5) as given by dT/dy=(dT/dy)i+aT(y−yi), where aT=7.1954×10−5 °C/year2 is the secular GMT acceleration and y−yi is the number of years of the change. The secular solar forcing due to 18% of the 11 yr solar cycle forcing of 0.19 W/m2 (0.08% of Total Solar Irradiance) was expressed as ΔF=0.18×0.19(y−yi)/11. Defining the climate sensitivity as λ=Δ(dT/dy)/ΔF=aT/(0.18×0.19/11)=0.023143 °C/year per W/m2 removed the shortcoming of the LM and integration of the model for dT/dy above and then simplifying gave a secular GMT‐solar forcing model given by ΔT=(dT/dy)i(y−yi)+(λ2/(2aT))(ΔF)2 that explained all of the observed global warming and increase in atmospheric CO2, sea level and ocean heat content. Therefore, for this nonlinear empirical model, invoking human influences to explain climate change was not required. The annual GMT model predicts a pause in global warming until 2040. Key Points: For a nonlinear empirical mathematical model, solar forcing explained all of the observed global warmingsFor doubling of the annual atmospheric CO2 concentration, the global mean temperature (GMT) increased by 1.4°C and the sea level by 0.37 mThe empirical model for the 30 yr GMT moving trends predicts deceleration to less than 0.1°C/decade for period 2007–2037 [ABSTRACT FROM AUTHOR]

Copyright of Journal of Neotropical Biology / Revista de Biologia Neotropical is the property of Journal of Neotropical Biology / Revista de Biologia Neotropical and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: To accurately assess the roles of agriculture in securing food security and maintaining environmental sustainability, it is essential to improve the representation of crop growth, development, and yield formation in global land models that traditionally focus on energy, water, carbon, and nitrogen exchanges between land and the atmosphere. In this study, a process‐based agricultural module has been coupled with the Dynamic Land Ecosystem Model (DLEM‐AG2.0) for assessing how multiple environmental factors (climate change, atmospheric CO2 concentration, tropospheric O3, and nitrogen deposition) and human activities (land use/cover change, nitrogen fertilizer use, and irrigation) have affected the crop growth, development, yield, carbon (C), nitrogen (N), and water cycles in agroecosystems. Here we describe the model structure for simulating crop growth, development, and yield formation in the DLEM‐AG2.0, and then we validate the model using field observations and a national yield survey for three major crops (wheat, maize, and rice) in China during 1980–2012. Results show that the DLEM‐AG2.0 is capable of simulating the dynamic processes of phenological development, leaf growth expansion, biomass accumulation, biomass allocation, and yield formation for wheat, maize, and rice with normalized root mean square errors of the simulations of less than 20%. Our model‐based yield estimation for the three major crops at the national scale for the period 1980–2012 is generally consistent with the national yield survey in China. The crop representation in the DLEM‐AG2.0 is flexible for extrapolating to a global scale after rigorous testing with both site‐specific and regional observations. Further advancement of agricultural modeling within the global land modeling framework will require consideration of human perception and behavior for adapting and mitigating global change. [ABSTRACT FROM AUTHOR]

Inaccurate estimate of the largest terrestrial carbon pool, soil organic carbon (SOC) stock, is the major source of uncertainty in simulating feedback of climate warming on ecosystem--atmosphere carbon dioxide exchange by process-based ecosystem and soil carbon models. Although the models need to simplify complex environmental processes of soil carbon sequestration, in a large mosaic of environments a missing key driver could lead to a modeling bias in predictions of SOC stock change. We aimed to evaluate SOC stock estimates of process-based models (Yasso07, Q, and CENTURY soil sub-model v4) against a massive Swedish forest soil inventory data set (3230 samples) organized by a recursive partitioning method into distinct soil groups with underlying SOC stock development linked to physicochemical conditions. For two-thirds of measurements all models predicted accurate SOC stock levels regardless of the detail of input data, e.g., whether they ignored or included soil properties. However, in fertile sites with high N deposition, high cation exchange capacity, or moderately increased soil water content, Yasso07 and Q models underestimated SOC stocks. In comparison to Yasso07 and Q, accounting for the site-specific soil characteristics (e. g. clay content and topsoil mineral N) by CENTURY improved SOC stock estimates for sites with high clay content, but not for sites with high N deposition. Our analysis suggested that the soils with poorly predicted SOC stocks, as characterized by the high nutrient status and well-sorted parent material, indeed have had other predominant drivers of SOC stabilization lacking in the models, presumably the mycorrhizal organic uptake and organo-mineral stabilization processes. Our results imply that the role of soil nutrient status as regulator of organic matter mineralization has to be re-evaluated, since correct SOC stocks are decisive for predicting future SOC change and soil CO2 efflux. [ABSTRACT FROM AUTHOR]

There is an increasing need to understand the pre-Quaternary warm climates, how climate-vegetation interactions functioned in the past, and how we can use this information to understand the present. Here we report vegetation modelling results for the Late Miocene (11-7 Ma) to study the mechanisms of vegetation dynamics and the role of different forcing factors that influence the spatial patterns of vegetation coverage. One of the key uncertainties is the atmospheric concentration of CO2 during past climates. Estimates for the last 20 million years range from 280 to 500 ppm. We simulated Late Miocene vegetation using two plausible CO2 concentrations, 280 ppm CO2 and 450 ppm CO2, with a dynamic global vegetation model (LPJ-GUESS) driven by climate input from a coupled AOGCM (Atmosphere-Ocean General Circulation Model). The simulated vegetation was compared to existing plant fossil data for the whole Northern Hemisphere. For the comparison we developed a novel approach that uses information of the relative dominance of different plant functional types (PFTs) in the palaeobotanical data to provide a quantitative estimate of the agreement between the simulated and reconstructed vegetation. Based on this quantitative assessment we find that pre-industrial CO2 levels are largely consistent with the presence of seasonal temperate forests in Europe (suggested by fossil data) and open vegetation in North America (suggested by multiple lines of evidence). This suggests that during the Late Miocene the CO2 levels have been relatively low, or that other factors that are not included in the models maintained the seasonal temperate forests and open vegetation. [ABSTRACT FROM AUTHOR]

We present a performance assessment of the European Integrated Carbon Observing System (ICOS) atmospheric network for constraining European biogenic CO2 fluxes (hereafter net ecosystem exchange, NEE). The performance of the network is assessed in terms of uncertainty in the fluxes, using a state-of-the-art mesoscale variational atmospheric inversion system assimilating hourly averages of atmospheric data to solve for NEE at 6 h and 0.5° resolution. The performance of the ICOS atmospheric network is also assessed in terms of uncertainty reduction compared to typical uncertainties in the flux estimates from ecosystem models, which are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of northern summer and winter conditions in July and in December 2007, respectively. These computations are based on a observing system simulation experiment (OSSE) framework. We analyzed the uncertainty in a 2-week-mean NEE as a function of the spatial scale with a focus on the model native grid scale (0.5°), the country scale and the European scale (including western Russia and Turkey). Several network configurations, going from 23 to 66 sites, and different configurations of the prior uncertainties and atmospheric model transport errors are tested in order to assess and compare the improvements that can be expected in the future from the extension of the network, from improved prior information or transport models. Assimilating data from 23 sites (a network comparable to presentday capability) with errors estimated from the present prior information and transport models, the uncertainty reduction on a 2-week-mean NEE should range between 20 and 50% for 0.5° resolution grid cells in the best sampled area encompassing eastern France and western Germany. At the European scale, the prior uncertainty in a 2-week-mean NEE is reduced by 50% (66%), down to ~43 TgC month-1 (26 TgC month-1) in July (December). Using a larger network of 66 stations, the prior uncertainty of NEE is reduced by the inversion by 64% (down to ~33 TgC month-1) in July and by 79%(down to ~15 TgC month-1) in December. When the results are integrated over the well-observed western European domain, the uncertainty reduction shows no seasonal variability. The effect of decreasing the correlation length of the prior uncertainty, or of reducing the transport model errors compared to their present configuration (when conducting real-data inversion cases) can be larger than that of the extension of the measurement network in areas where the 23 station observation network is the densest. We show that with a configuration of the ICOS atmospheric network containing 66 sites that can be expected on the long-term, the uncertainties in a 2-week-mean NEE will be reduced by up to 50-80% for countries like Finland, Germany, France and Spain, which could significantly improvement (and at least a high complementarity to) our knowledge of NEE derived from biomass and soil carbon inventories at multi-annual scales. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Chemistry is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multiannual satellite measurements of tropospheric NO2 columns are used for evaluation of CO2 emission changes in China in the period from 1996 to 2008. Indirect top-down annual estimates of CO2 emissions are derived from the satellite NO2 column measurements by means of a simple inverse modeling procedure involving simulations performed with the CHIMERE mesoscale chemistry- transport model and the CO2-to-NOx emission ratios from the Emission Database for Global Atmospheric Research (EDGAR) global anthropogenic emission inventory and Regional Emission Inventory in Asia (REAS). Exponential trends in the normalized time series of annual emissions are evaluated separately for the periods from 1996 to 2001 and from 2001 to 2008. The results indicate that the both periods manifest strong positive trends in the CO2 emissions, and that the trend in the second period was significantly larger than the trend in the first period. Specifically, the trends in the first and second periods are best estimated to be in the range from 3.7 to 8.3 and from 11.0 to 13.2% per year, respectively, taking into account statistical uncertainties and differences between the CO2-to-NOx emission ratios from the EDGAR and REAS inventories. Comparison of our indirect top-down estimates of the CO2 emission changes with the corresponding bottom-up estimates provided by the EDGAR (version 4.2) and Global Carbon Project (GCP) glomal emission inventories reveals that while acceleration of the CO2 emission growth in the considered period is a common feature of both kinds of estimates, nonlinearity in the CO2 emission changes may be strongly exaggerated in the global emission inventories. Specifically, the atmospheric NO2 observations do not confirm the existence of a sharp bend in the emission inventory data time series in the period from 2000 to 2002. A significant quantitative difference is revealed between the bottom-up and indirect top-down estimates of the CO2 emission trend in the period from 1996 to 2001 (specifically, the trend was not positive according to the global emission inventories, but is strongly positive in our estimates). These results confirm the findings of earlier studies that indicated probable large uncertainties in the energy production and other activity data for China from international energy statistics used as the input information in the global emission inventories. For the period from 2001 to 2008, some quantitative differences between the different kinds of estimates are found to be in the range of possible systematic uncertainties associated with our estimation method. In general, satellite measurements of tropospheric NO2 are shown to be a useful source of information on CO2 sources collocated with sources of nitrogen oxides; the corresponding potential of these measurements should be exploited further in future studies. [ABSTRACT FROM AUTHOR]

Terrestrial carbon (C) cycle models applied for climate projections simulate a strong increase in net primary productivity (NPP) due to elevated atmospheric CO2 concentration during the 21st century. These models usually neglect the limited availability of nitrogen (N) and phosphorus (P), nutrients that commonly limit plant growth and soil carbon turnover. To investigate how the projected C sequestration is altered when stoichiometric constraints on C cycling are considered, we incorporated a P cycle into the land surface model JSBACH (Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg), which already includes representations of coupled C and N cycles. The model reveals a distinct geographic pattern of P and N limitation. Under the SRES (Special Report on Emissions Scenarios) A1B scenario, the accumulated land C uptake between 1860 and 2100 is 13% (particularly at high latitudes) and 16% (particularly at low latitudes) lower in simulations with N and P cycling, respectively, than in simulations without nutrient cycles. The combined effect of both nutrients reduces land C uptake by 25% compared to simulations without N or P cycling. Nutrient limitation in general may be biased by the model simplicity, but the ranking of limitations is robust against the parameterization and the inflexibility of stoichiometry. After 2100, increased temperature and high CO2 concentration cause a shift from N to P limitation at high latitudes, while nutrient limitation in the tropics declines. The increase in P limitation at high-latitudes is induced by a strong increase in NPP and the low P sorption capacity of soils, while a decline in tropical NPP due to high autotrophic respiration rates alleviates N and P limitations. The quantification of P limitation remains challenging. The poorly constrained processes of soil P sorption and biochemical mineralization are identified as the main uncertainties in the strength of P limitation. Even so, our findings indicate that global land C uptake in the 21st century is likely overestimated in models that neglect P and N limitations. In the long term, insufficient P availability might become an important constraint on C cycling at high latitudes. Accordingly, we argue that the P cycle must be included in global models used for C cycle projections. [ABSTRACT FROM AUTHOR]

Increasing accumulation of CO2 in the atmosphere has led to calls for terrestrial mechanisms for CO2 abatement and given that soils represent the largest terrestrial body of C on Earth, there is a great deal of interest in soils as a sink for atmospheric C. This emphasis on sequestration in boreal forest soils is understandable given the sheer mass of this C reservoir (∼1700 Pg of C) but diverts our attention from the importance of soil C in soil physical, chemical and biotic functions, and importantly, it ignores the possibility that soils may also represent a source of C. In this review, we address these issues through a discussion of the size and character of boreal forest soil C pool, its role in ecosystem function, the potential impacts of climate change on soil C, efforts to model these processes and the role of soil C in boreal resilience to the impacts of climate change. Soil C is fundamental to ecosystem function in terms of improving soil physical properties, increasing soil biotic activity and enhancing insulation all of which improve site productivity. Managing upland soils for C sequesteration will achieve little in terms of offsetting fossil fuel emissions but would likely improve soil quality. Most of the C stored in the boreal biome is found in permafrost and wetland soils and events related to climatic change could shift these soils from C sink to C source. Melting of permafrost soils with predicted warming trends within the circumpolar region could result in the release of 30–60 Pg C by the year 2040. Such predictions, however, are limited by uncertainty in both climatic changes and soil response to these changes. Prediction of shifts in soil C dynamics with climate change relies on our ability to link C transformations to N dynamics and climatic variables. Improvement in ecosystem models will advance our ability to assess the resilience of the boreal biome under future climatic conditions. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) is a potent greenhouse gas which also contributes to the depletion of stratospheric ozone (O3). However, the magnitude and underlying mechanisms for the spatiotemporal variations in the terrestrial sources of N2O are still far from certain. Using a process-based ecosystem model (DLEM -- the Dynamic Land Ecosystem Model) driven by multiple global change factors, including climate variability, nitrogen (N) deposition, rising atmospheric carbon dioxide (CO2), tropospheric O3 pollution, N fertilizer application, and land conversion, this study examined the spatial and temporal variations in terrestrial N2O flux over North America and further attributed these variations to various driving factors. From 1979 to 2010, the North America cumulatively emitted 53.9±0.9 TgN2ON (1 Tg=1012 g), of which global change factors contributed 2.4±0.9 TgN2O-N, and baseline emission contributed 51.5±0.6 TgN2O-N. Climate variability, N deposition, O3 pollution, N fertilizer application, and land conversion increased N2O emission while the elevated atmospheric CO2 posed opposite effect at continental level; the interactive effect among multiple factors enhanced N2O emission over the past 32 yr. N input, including N fertilizer application in cropland and N deposition, and multi-factor interaction dominated the increases in N2O emission at continental level. At country level, N fertilizer application and multi-factor interaction made large contribution to N2O emission increase in the United States of America (USA). The climate variability dominated the increase in N2O emission from Canada. N inputs and multiple factors interaction made large contribution to the increases in N2O emission from Mexico. Central and southeastern parts of the North America -- including central Canada, central USA, southeastern USA, and all of Mexico -- experienced increases in N2O emission from 1979 to 2010. The fact that climate variability and multi-factor interaction largely controlled the inter-annual variations in terrestrial N2O emission at both continental and country levels indicate that projected changes in the global climate system may substantially alter the regime of N2O emission from terrestrial ecosystems during the 21st century. Our study also showed that the interactive effect among global change factors may significantly affect N2O flux, and more field experiments involving multiple factors are urgently needed. [ABSTRACT FROM AUTHOR]

The article presents a study which explores the Atmospheric CO2 Observations from Space (ACOS) X CO2 retrieval algorithm of the U.S. National Aeronautics and Space Administration (NASA). Realistic simulations were used to evaluate systematic and random errors in the retrieved ACOS algorithms, such as the impacts of realistic cloud screening and post-retrieval filters. The study suggests that post-retrieval filters are essential in eliminating poorest retrievals.

Soil organic carbon plays a major role in the global carbon budget, and can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Changes in soil organic carbon (SOC) stocks are now taken into account in international negotiations regarding climate change. Consequently, developing sampling schemes and models for estimating the spatial distribution of SOC stocks is a priority. The French soil monitoring network has been established on a 16km x 16km grid and the first sampling campaign has recently been completed, providing around 2200 measurements of stocks of soil organic carbon, obtained through an in situ composite sampling, uniformly distributed over the French territory. We calibrated a boosted regression tree model on the observed stocks, modelling SOC stocks as a function of other variables such as climatic parameters, vegetation net primary productivity, soil properties and land use. The calibrated model was evaluated through cross-validation and eventually used for estimating SOC stocks for mainland France. Two other models were calibrated on forest and agricultural soils separately, in order to assess more precisely the influence of pedo-climatic variables on SOC for such soils. The boosted regression tree model showed good predictive ability, and enabled quantification of relationships between SOC stocks and pedo-climatic variables (plus their interactions) over the French territory. These relationships strongly depended on the land use, and more specifically, differed between forest soils and cultivated soil. The total estimate of SOC stocks in France was 3.260 ± 0.872 PgC for the first 30cm. It was compared to another estimate, based on the previously published European soil organic carbon and bulk density maps, of 5.303 PgC. We demonstrate that the present estimate might better represent the actual SOC stock distributions of France, and consequently that the previously published approach at the European level greatly overestimates SOC stocks. [ABSTRACT FROM AUTHOR]

The article discusses the optimal estimation method for the accurate retrieval of carbon dioxide in the presence of thin clouds. It states that the proposed approach is developed to examine near-infrared nadir measurements of Sciamachy instrument in the carbon dioxide and dioxide-A absorption band. Findings reveal that the method has potential to minimize the uncertainties of the instrument.

The authors investigate how the global monsoon (GM) precipitation responds to the external and anthropogenic forcing in the last millennium by analyzing a pair of control and forced millennium simulations with the ECHAM and the global Hamburg Ocean Primitive Equation (ECHO-G) coupled ocean–atmosphere model. The forced run, which includes the solar, volcanic, and greenhouse gas forcing, captures the major modes of precipitation climatology comparably well when contrasted with those captured by the NCEP reanalysis. The strength of the modeled GM precipitation in the forced run exhibits a significant quasi-bicentennial oscillation. Over the past 1000 yr, the simulated GM precipitation was weak during the Little Ice Age (1450–1850) with the three weakest periods occurring around 1460, 1685, and 1800, which fell in, respectively, the Spörer Minimum, Maunder Minimum, and Dalton Minimum periods of solar activity. Conversely, strong GM was simulated during the model Medieval Warm Period (ca. 1030–1240). Before the industrial period, the natural variations in the total amount of effective solar radiative forcing reinforce the thermal contrasts both between the ocean and continent and between the Northern and Southern Hemispheres resulting in the millennium-scale variation and the quasi-bicentennial oscillation in the GM index. The prominent upward trend in the GM precipitation occurring in the last century and the notable strengthening of the global monsoon in the last 30 yr (1961–90) appear unprecedented and are due possibly in part to the increase of atmospheric carbon dioxide concentration, though the authors' simulations of the effects from recent warming may be overestimated without considering the negative feedbacks from aerosols. The simulated change of GM in the last 30 yr has a spatial pattern that differs from that during the Medieval Warm Period, suggesting that global warming that arises from the increases of greenhouse gases and the input solar forcing may have different effects on the characteristics of GM precipitation. It is further noted that GM strength has good relational coherence with the temperature difference between the Northern and Southern Hemispheres, and that on centennial time scales the GM strength responds more directly to the effective solar forcing than the concurrent forced response in global-mean surface temperature. [ABSTRACT FROM AUTHOR]

In Summer 2002 eddy covariance flux measurements of CO2 were performed over a dense urban surface. The month-long measurements were carried out in the framework of the Basel Urban Boundary Layer Experiment (BUBBLE). Two Li7500 open path analysers were installed at z/z H = 1.0 and 2.2 above a street canyon with z H the average building height of 14.6 m and z the height above street level. Additionally, profiles of CO2 concentration were sampled at 10 heights from street level up to 2 z H . The minimum and maximum of the average diurnal course of CO2 concentration at 2 z H were 362 and 423 ppmv in late afternoon and early morning, respectively. Daytime CO2 concentrations were not correlated to local sources, e.g. the minimum occurred together with the maximum in traffic load. During night-time CO2 is in general accumulated, except when inversion development is suppressed by frontal passages. CO2 concentrations were always decreasing with height and correspondingly, the fluxes – on average – always directed upward. At z/z H = 2.2 low values of about 3 µmol m−2 s−1 were measured during the second half of the night. During daytime average values reached up to 14 µmol m−2 s−1. The CO2 fluxes are well correlated with the traffic load, with their maxima occurring together in late afternoon. Daytime minimum CO2 concentrations fell below regional background values. Besides vertical mixing and entrainment, it is suggested that this is also due to advection of rural air with reduced CO2 concentration. Comparison with other urban observations shows a large range of differences among urban sites in terms of both CO2 fluxes and concentrations. [ABSTRACT FROM AUTHOR]

A measuring campaign was conducted in the street canyon 'Runeberg street' in Helsinki in 1997. Hourly concentrations of carbon monoxide (CO), nitrogen oxides (NOX), nitrogen dioxide (NO2) and ozone (O3) were measured at the street and roof levels, and the relevant hourly meteorological parameters were measured at the roof level. The hourly street level measurements and on-site electronic traffic counts were conducted during the whole year 1997, and roof level measurements were conducted during approximately two months, from 3 March to 30 April in 1997. The Operational Street Pollution Model (OSPM) was used to calculate the street concentrations and the results were compared with the measurements. The overall agreement between measured and predicted concentrations was good for CO and NOx, but the model slightly overestimated the measured concentrations of NO2. The database, which contains all measured and predicted data, is available for a further testing of other street canyon dispersion models. [ABSTRACT FROM AUTHOR]

Presents research which quantified the dynamic variations in ecosystem carbon fluxes induced by changes in atmospheric CO2 and climate from 1861 to 2070. Use of a terrestrial biogeochemical model; Coupling of terrestrial ecosystems and the climate system; Carbon cycling; Prior research; Predictions about global net CO2 production; Response and influence of terrestrial ecosystem carbon fluxes.