scholarly journals The Adaptable 4A Inversion (5AI): description and first <i>X</i><sub>CO<sub>2</sub></sub> retrievals from Orbiting Carbon Observatory-2 (OCO-2) observations

2021 ◽  
Vol 14 (6) ◽  
pp. 4689-4706
Author(s):  
Matthieu Dogniaux ◽  
Cyril Crevoisier ◽  
Raymond Armante ◽  
Virginie Capelle ◽  
Thibault Delahaye ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Total Carbon ◽  
Computational Time ◽  
Dry Air ◽  
Global Challenge ◽  
Spectroscopic Information ◽  
Residual Correction ◽  
The Impact

Abstract. A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Space-borne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on the Optimal Estimation algorithm, relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry air mole fraction of carbon dioxide (XCO2) from a sample of measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission. Those have been selected as a compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI XCO2 retrievals successfully capture the latitudinal variations of CO2 and its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases.

scholarly journals The Adaptable 4A Inversion (5AI): Description and first XCO<sub>2</sub> retrievals from OCO-2 observations

2020 ◽  
Author(s):  
Matthieu Dogniaux ◽  
Cyril Crevoisier ◽  
Raymond Armante ◽  
Virginie Capelle ◽  
Thibault Delahaye ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Optimal Estimation ◽  
Total Carbon ◽  
Sources And Sinks ◽  
Dry Air ◽  
Global Challenge ◽  
Spectral Residual ◽  
Spectroscopic Information ◽  
Global Coverage ◽  
Residual Correction

Abstract. A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O2 A-band. For airmasses below 3.0, XCO2 retrievals successfully capture the latitudinal variations of CO2, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated – observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases.

scholarly journals An improved carbon greenhouse gas simulation in GEOS-Chem version 12.1.1

2021 ◽  
Author(s):  
Beata Bukosa ◽  
Jenny Fisher ◽  
Nicholas Deutscher ◽  
Dylan Jones
Keyword(s):  
Greenhouse Gases ◽  
Greenhouse Gas ◽  
Chemical Transport ◽  
Total Carbon ◽  
Anthropogenic Sources ◽  
Reference Network ◽  
Chemical Production ◽  
Gas Fluxes ◽  
Greenhouse Gas Fluxes ◽  
The Impact

Abstract. Understanding greenhouse gas–climate processes and feedbacks is a fundamental step in understanding climate variability and its links to greenhouse gas fluxes. Chemical transport models are the primary tool for linking greenhouse gas fluxes to their atmospheric abundances. Hence accurate simulations of greenhouse gases are essential. Here, we present a new simulation in the GEOS-Chem chemical transport model that couples the two main greenhouse gases: carbon dioxide (CO2) and methane (CH4), along with the indirect effects of carbon monoxide (CO), based on their chemistry. Our updates include the online calculation of the chemical production of CO from CH4 and the online production of CO2 from CO, both of which were handled offline in the previous versions of these simulations. We discuss differences between the offline (uncoupled) and online (coupled) calculation of the chemical terms and perform a sensitivity simulation to identify the impact of OH on the results. We compare our results with surface measurements from the NOAA Global Greenhouse Gas Reference Network (NOAA GGGRN), total column measurements from the Total Carbon Column Observing Network (TCCON) and aircraft measurements from the Atmospheric Tomography Mission (ATom). Relative to the standard uncoupled simulation, our coupled results show better agreement with measurements. We use the remaining measurement-model differences to identify sources and sinks that are over or underestimated in the model. We find underestimated OH fields when calculating the CH4 loss and CO production from CH4. Biomass burning emissions and secondary production are underestimated for CO in the Southern Hemisphere and we find enhanced anthropogenic sources in the Northern Hemisphere. We also find significantly stronger chemical production of CO2 in tropical land regions, especially in the Amazon. The model-measurement differences also highlight biases in the calculation of CH4 in the stratosphere and in vertical mixing that impacts all three gases.

scholarly journals The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO<sub>2</sub> and CH<sub>4</sub> retrieval algorithm products with measurements from the TCCON network

2013 ◽  
Vol 6 (5) ◽  
pp. 8679-8741 ◽  
Author(s):  
B. Dils ◽  
M. Buchwitz ◽  
M. Reuter ◽  
O. Schneising ◽  
H. Boesch ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Weighting Function ◽  
Optimal Estimation ◽  
Total Carbon ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Change Initiative ◽  
The University

Abstract. Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS and SCIAMACHY instruments on board GOSAT and ENVISAT using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier Transform Spectrometers (FTS) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the Weighting Function Modified Differential Optical Absorption Spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen Optimal Estimation DOAS algorithm (BESD, University of Bremen), the Iterative Maximum A Posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called Round Robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "Greenhouse Gases" (GHG). For CO2, all algorithms reach the precision requirements for inverse modelling (< 8 ppb), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For CH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fail to meet the < 34 ppb threshold, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 ppb and 10.5 ppb respectively.

The Effect of Diversity and the Mediating Role of Elaboration on Multidisciplinary Greenhouse Gas Assurance Team Effectiveness

2018 ◽  
Vol 31 (1) ◽  
pp. 81-96 ◽  
Author(s):  
Erboon Ekasingh ◽  
Roger Simnett ◽  
Wendy J. Green
Keyword(s):  
Greenhouse Gas ◽  
Team Effectiveness ◽  
Team Member ◽  
Multidisciplinary Teams ◽  
Team Processes ◽  
Mediating Role ◽  
Audit Firms ◽  
The Impact ◽  
Educational Diversity

ABSTRACT Greenhouse gas (GHG) assurance is increasingly used by companies as a means to increase stakeholder confidence in the quality of externally reported carbon emissions. The multidisciplinary nature of these engagements means that assurance is performed primarily by multidisciplinary teams. Prior research suggests the effectiveness of such teams could be affected by team composition and team processes. We employ a retrospective field study to examine the impact of educational diversity and team member elaboration on multidisciplinary GHG assurance team effectiveness. Results show that team processes such as sufficiency of elaboration on different team member perspectives significantly increases the perceived effectiveness of the teams. While educational diversity is not found to directly improve perceived team effectiveness, it is found to have a positive effect through increasing perceived sufficiency of elaboration. These findings have important implications for standard setters and audit firms undertaking GHG assurance engagements.

scholarly journals Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

2020 ◽  
Vol 12 (3) ◽  
pp. 528 ◽  
Author(s):  
Jingye Li ◽  
Jian Gong ◽  
Jean-Michel Guldmann ◽  
Shicheng Li ◽  
Jie Zhu
Keyword(s):  
Land Use ◽  
Tibetan Plateau ◽  
Carbon Storage ◽  
Sharp Decrease ◽  
Total Carbon ◽  
Qinghai Lake ◽  
Lake Basin ◽  
Ecosystem Carbon ◽  
Trade Offs ◽  
The Impact

Land use/cover change (LUCC) has an important impact on the terrestrial carbon cycle. The spatial distribution of regional carbon reserves can provide the scientific basis for the management of ecosystem carbon storage and the formulation of ecological and environmental policies. This paper proposes a method combining the CA-based FLUS model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to assess the temporal and spatial changes in ecosystem carbon storage due to land-use changes over 1990–2015 in the Qinghai Lake Basin (QLB). Furthermore, future ecosystem carbon storage is simulated and evaluated over 2020–2030 under three scenarios of natural growth (NG), cropland protection (CP), and ecological protection (EP). The long-term spatial variations in carbon storage in the QLB are discussed. The results show that: (1) Carbon storage in the QLB decreased at first (1990–2000) and increased later (2000–2010), with total carbon storage increasing by 1.60 Tg C (Teragram: a unit of mass equal to 1012 g). From 2010 to 2015, carbon storage displayed a downward trend, with a sharp decrease in wetlands and croplands as the main cause; (2) Under the NG scenario, carbon reserves decrease by 0.69 Tg C over 2020–2030. These reserves increase significantly by 6.77 Tg C and 7.54 Tg C under the CP and EP scenarios, respectively, thus promoting the benign development of the regional ecological environment. This study improves our understanding on the impact of land-use change on carbon storage for the QLB in the northeastern Qinghai–Tibetan Plateau (QTP).

scholarly journals Effect of Organic Amendment Addition on Soil Properties, Greenhouse Gas Emissions and Grape Yield in Semi-Arid Vineyard Agroecosystems

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1477
Author(s):  
Antonio Marín-Martínez ◽  
Alberto Sanz-Cobeña ◽  
Mª Angeles Bustamante ◽  
Enrique Agulló ◽  
Concepción Paredes
Keyword(s):  
Soil Fertility ◽  
Soil Properties ◽  
Greenhouse Gas ◽  
Co2 Emissions ◽  
Organic Amendments ◽  
Organic N ◽  
N2o Emissions ◽  
The Impact ◽  
Semi Arid ◽  
Grape Yield

In semi-arid vineyard agroecosystems, highly vulnerable in the context of climate change, the soil organic matter (OM) content is crucial to the improvement of soil fertility and grape productivity. The impact of OM, from compost and animal manure, on soil properties (e.g., pH, oxidisable organic C, organic N, NH4+-N and NO3−-N), grape yield and direct greenhouse gas (GHG) emission in vineyards was assessed. For this purpose, two wine grape varieties were chosen and managed differently: with a rain-fed non-trellising vineyard of Monastrell, a drip-irrigated trellising vineyard of Monastrell and a drip-irrigated trellising vineyard of Cabernet Sauvignon. The studied fertiliser treatments were without organic amendments (C), sheep/goat manure (SGM) and distillery organic waste compost (DC). The SGM and DC treatments were applied at a rate of 4600 kg ha−1 (fresh weight, FW) and 5000 kg ha−1 FW, respectively. The use of organic amendments improved soil fertility and grape yield, especially in the drip-irrigated trellising vineyards. Increased CO2 emissions were coincident with higher grape yields and manure application (maximum CO2 emissions = 1518 mg C-CO2 m−2 d−1). In contrast, N2O emissions, mainly produced through nitrification, were decreased in the plots showing higher grape production (minimum N2O emissions = −0.090 mg N2O-N m−2 d−1). In all plots, the CH4 fluxes were negative during most of the experiment (−1.073−0.403 mg CH4-C m−2 d−1), indicating that these ecosystems can represent a significant sink for atmospheric CH4. According to our results, the optimal vineyard management, considering soil properties, yield and GHG mitigation together, was the use of compost in a drip-irrigated trellising vineyard with the grape variety Monastrell.

scholarly journals Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1107
Author(s):  
Stefano d’Ambrosio ◽  
Roberto Finesso ◽  
Gilles Hardy ◽  
Andrea Manelli ◽  
Alessandro Mancarella ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Rapid Prototyping ◽  
Nitrogen Oxide ◽  
Thermal State ◽  
Pollutant Emissions ◽  
Computational Time ◽  
Nox Emissions ◽  
Model Based ◽  
Brake Mean Effective Pressure ◽  
The Impact

In the present paper, a model-based controller of engine torque and engine-out Nitrogen oxide (NOx) emissions, which was previously developed and tested by means of offline simulations, has been validated on a FPT F1C 3.0 L diesel engine by means of rapid prototyping. With reference to the previous version, a new NOx model has been implemented to improve robustness in terms of NOx prediction. The experimental tests have confirmed the basic functionality of the controller in transient conditions, over different load ramps at fixed engine speeds, over which the average RMSE (Root Mean Square Error) values for the control of NOx emissions were of the order of 55–90 ppm, while the average RMSE values for the control of brake mean effective pressure (BMEP) were of the order of 0.25–0.39 bar. However, the test results also highlighted the need for further improvements, especially concerning the effect of the engine thermal state on the NOx emissions in transient operation. Moreover, several aspects, such as the check of the computational time, the impact of the controller on other pollutant emissions, or on the long-term engine operations, will have to be evaluated in future studies in view of the controller implementation on the engine control unit.

27 Greenhouse Gas Emissions and a Partial Life Cycle Assessment When Growing Pigs Are Fed High Wheat Millrun Diets

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 24-25
Author(s):  
Agbee L Kpogo ◽  
Jismol Jose ◽  
Josiane Panisson ◽  
Bernardo Predicala ◽  
Alvin Alvarado ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Greenhouse Gas ◽  
Growing Pigs ◽  
Pork Production ◽  
Feed Production ◽  
Farm Model ◽  
And Performance ◽  
Carbon Dioxide Co2 ◽  
The Impact

Abstract The impact of feeding growing pigs with high wheat millrun diets on the global warming potential (GWP) of pork production was investigated. In study 1, a 2 × 2 factorial arrangement of wheat millrun (0 or 30%) and multi-carbohydrase enzyme (0 or 1 mg kg-1) as main effects was utilized. For each of 16 reps, 6 pigs (60.2±2.2 kg BW) were housed in environmental chambers for 14d. Air samples were collected and analyzed for carbon dioxide (CO2); nitrous oxide (N2O); and methane (CH4). In study 2, data from study 1 and performance data obtained from a previous feeding trial were utilized in a life cycle assessment (LCA) framework that included feed production. The Holos farm model (Agriculture and Agri-Food Canada, Lethbridge. AB) was used to estimate emissions from feed production. In study 1, total manure output from pigs fed 30% wheat millrun diets was 30% greater than pigs on the 0% wheat millrun diets (P &lt; 0.05), however, Feeding diets with 30% millrun did not affect greenhouse gas (GHG) output (CH4, 4.7, 4.9; N2O, 0.45, 0.42; CO2, 1610, 1711; mg s-1 without or with millrun inclusion, respectively; P &gt; 0.78). Enzyme supplementation had no effect on GHG production (CH4, 4.5, 5.1; N2O, 0.46, 0.42; CO2, 1808, 1513; mg s-1 without or with enzymes, respectively; P &gt; 0.51). In study 2, the LCA indicated that the inclusion of 30% wheat millrun in diets for growing pigs resulted in approximately a 25% reduction in GWP when compared to the no wheat millrun diets. Our results demonstrate that 30% wheat millrun did not increase GHG output from the pigs, and thus the inclusion of wheat millrun in diets of growing pigs can reduce the GWP of pork production.

scholarly journals Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?

2021 ◽  
Vol 18 (11) ◽  
pp. 5567
Author(s):  
Moneim Massar ◽  
Imran Reza ◽  
Syed Masiur Rahman ◽  
Sheikh Muhammad Habib Abdullah ◽  
Arshad Jamal ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Autonomous Vehicles ◽  
Significant Contribution ◽  
Ghg Emissions ◽  
Interval Estimate ◽  
Modal Shift ◽  
Vehicle Mile Travel ◽  
Emission Changes ◽  
Negative Impacts ◽  
The Impact

The potential effects of autonomous vehicles (AVs) on greenhouse gas (GHG) emissions are uncertain, although numerous studies have been conducted to evaluate the impact. This paper aims to synthesize and review all the literature regarding the topic in a systematic manner to eliminate the bias and provide an overall insight, while incorporating some statistical analysis to provide an interval estimate of these studies. This paper addressed the effect of the positive and negative impacts reported in the literature in two categories of AVs: partial automation and full automation. The positive impacts represented in AVs’ possibility to reduce GHG emission can be attributed to some factors, including eco-driving, eco traffic signal, platooning, and less hunting for parking. The increase in vehicle mile travel (VMT) due to (i) modal shift to AVs by captive passengers, including elderly and disabled people and (ii) easier travel compared to other modes will contribute to raising the GHG emissions. The result shows that eco-driving and platooning have the most significant contribution to reducing GHG emissions by 35%. On the other side, easier travel and faster travel significantly contribute to the increase of GHG emissions by 41.24%. Study findings reveal that the positive emission changes may not be realized at a lower AV penetration rate, where the maximum emission reduction might take place within 60–80% of AV penetration into the network.

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