scholarly journals Global inverse modeling of CH<sub>4</sub> sources and sinks: an overview of methods

2017 ◽  
Vol 17 (1) ◽  
pp. 235-256 ◽  
Author(s):  
Sander Houweling ◽  
Peter Bergamaschi ◽  
Frederic Chevallier ◽  
Martin Heimann ◽  
Thomas Kaminski ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Process Model ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Data Uncertainty ◽  
Grand Challenge ◽  
Sources And Sinks ◽  
Multiple Data ◽  
Methodological Aspects ◽  
Atmospheric Transport Modeling

Abstract. The aim of this paper is to present an overview of inverse modeling methods that have been developed over the years for estimating the global sources and sinks of CH4. It provides insight into how techniques and estimates have evolved over time and what the remaining shortcomings are. As such, it serves a didactical purpose of introducing apprentices to the field, but it also takes stock of developments so far and reflects on promising new directions. The main focus is on methodological aspects that are particularly relevant for CH4, such as its atmospheric oxidation, the use of methane isotopologues, and specific challenges in atmospheric transport modeling of CH4. The use of satellite retrievals receives special attention as it is an active field of methodological development, with special requirements on the sampling of the model and the treatment of data uncertainty. Regional scale flux estimation and attribution is still a grand challenge, which calls for new methods capable of combining information from multiple data streams of different measured parameters. A process model representation of sources and sinks in atmospheric transport inversion schemes allows the integrated use of such data. These new developments are needed not only to improve our understanding of the main processes driving the observed global trend but also to support international efforts to reduce greenhouse gas emissions.

scholarly journals Global inverse modeling of CH<sub>4</sub> sources and sinks: An overview of methods

2016 ◽  
Author(s):  
Sander Houweling ◽  
Peter Bergamaschi ◽  
Frederic Chevallier ◽  
Martin Heimann ◽  
Thomas Kaminski ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Process Model ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Data Uncertainty ◽  
Grand Challenge ◽  
Sources And Sinks ◽  
Multiple Data ◽  
Methodological Aspects ◽  
Atmospheric Transport Modeling

Abstract. The aim of this paper is to present an overview of inverse modeling methods that have been developed over the years for estimating the global sources and sinks of CH4. It provides insight into how techniques and estimates have evolved over time, and what the remaining shortcomings are. As such, it serves a didactical purpose of introducing apprentices to the field, but it also takes stock of the developments so far and reflects on promising new directions. The main focus is on methodological aspects that are particularly relevant for CH4, such as its atmospheric oxidation, the use of methane isotopologues, and specific challenges in atmospheric transport modeling of CH4. The use of satellite retrievals receives special attention, as it is an active field of methodological development, with special requirements on the sampling of the model and the treatment of data uncertainty. Regional scale flux estimation and attribution is still a grand challenge, which calls for new methods capable of combining information from multiple data streams of different measured parameters. A process model representation of sources and sinks in atmospheric transport inversion schemes allows the integrated use of such data. These new developments are needed not only to improve our understanding of the main processes driving the observed global trend, but also to support international efforts to reduce greenhouse gas emissions.

scholarly journals Atmospheric Transport Modeling with 3D Lagrangian Dispersion Codes Compared with SF6 Tracer Experiments at Regional Scale

2007 ◽  
Vol 2007 ◽  
pp. 1-13 ◽  
Author(s):  
François Van Dorpe ◽  
Bertrand Iooss ◽  
Vladimir Semenov ◽  
Olga Sorokovikova ◽  
Alexey Fokin ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Atmospheric Dispersion ◽  
Large Field ◽  
Dispersion Modeling ◽  
Order Of Magnitude ◽  
Lagrangian Dispersion ◽  
Atmospheric Transport Modeling ◽  
Tracer Experiments

The results of four gas tracer experiments of atmospheric dispersion on a regional scale are used for the benchmarking of two atmospheric dispersion modeling codes, MINERVE-SPRAY (CEA), and NOSTRADAMUS (IBRAE). The main topic of this comparison is to estimate the Lagrangian code capability to predict the radionuclide atmospheric transfer on a large field, in the case of risk assessment of nuclear power plant for example. For the four experiments, the results of calculations show a rather good agreement between the two codes, and the order of magnitude of the concentrations measured on the soil is predicted. Simulation is best for sampling points located ten kilometers from the source, while we note a divergence for more distant points results (difference in concentrations by a factor 2 to 5). This divergence may be explained by the fact that, for these four experiments, only one weather station (near the point source) was used on a field of 10 000 km2, generating the simulation of a uniform wind field throughout the calculation domain.

scholarly journals Sensitivity of the recent methane budget to LMDz sub-grid-scale physical parameterizations

2015 ◽  
Vol 15 (17) ◽  
pp. 9765-9780 ◽  
Author(s):  
R. Locatelli ◽  
P. Bousquet ◽  
M. Saunois ◽  
F. Chevallier ◽  
C. Cressot
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Deep Convection ◽  
Regional Scale ◽  
Global Scale ◽  
Methane Emissions ◽  
Data Sets ◽  
Methane Budget ◽  
Physical Parameterizations

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modeling are gaining additional constraining data but facing new challenges. The chemical transport model (CTM) linking the flux space to methane mixing ratio space must be able to represent these different types of atmospheric constraints for providing consistent flux estimations. Here we quantify the impact of sub-grid-scale physical parameterization errors on the global methane budget inferred by inverse modeling. We use the same inversion setup but different physical parameterizations within one CTM. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer (PBL) are tested. Different atmospheric methane data sets are used as constraints (surface observations or satellite retrievals). At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid-scale parameterizations. Inversions using satellite total-column mixing ratios retrieved by GOSAT are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradients in methane emissions, indicating a slower interhemispheric exchange. At regional scale, the use of different sub-grid-scale parameterizations induces uncertainties ranging from 1.2 % (2.7 %) to 9.4 % (14.2 %) of methane emissions when using only surface measurements from a background (or an extended) surface network. Moreover, spatial distribution of methane emissions at regional scale can be very different, depending on both the physical parameterizations used for the modeling of the atmospheric transport and the observation data sets used to constrain the inverse system. When using only satellite data from GOSAT, we show that the small biases found in inversions using a coarser version of the transport model are actually masking a poor representation of the stratosphere–troposphere methane gradient in the model. Improving the stratosphere–troposphere gradient reveals a larger bias in GOSAT CH4 satellite data, which largely amplifies inconsistencies between the surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relative to physical parameterizations included in CTMs.

Kriging-based Mapping of Space-borne CO2 Measurements by Combining Emission Inventory and Atmospheric Transport Modeling

2020 ◽  
Author(s):  
Shrutilipi Bhattacharjee ◽  
Jia Chen ◽  
Li Jindun ◽  
Xinxu Zhao
Keyword(s):  
Atmospheric Transport ◽  
Interpolation Method ◽  
Regional Scale ◽  
Local Scale ◽  
Transport Modeling ◽  
Study Region ◽  
Spatial Coverage ◽  
Multiple Covariates ◽  
Temporal Measurement ◽  
Atmospheric Transport Modeling

&lt;p&gt;Atmospheric CO&lt;sub&gt;2&lt;/sub&gt; measurement has proven its appositeness for different applications in carbon cycle science. Many satellites are currently measuring the atmospheric CO&lt;sub&gt;2&lt;/sub&gt; concentration worldwide, for example, NASA&amp;#8217;s Orbiting Carbon Observatory-2 (OCO-2), Exploratory Satellite for Atmospheric CO&lt;sub&gt;2&lt;/sub&gt; (TanSat), Japanese Greenhouse gases Observing SATellite (GOSAT), and Environmental Satellite (ENVISAT). The OCO-2 measures the column-averaged CO&lt;sub&gt;2&lt;/sub&gt; dry air mole fractions (XCO&lt;sub&gt;2&lt;/sub&gt;) in the atmosphere as contiguous parallelogram footprints, each having area up to about 3 km&lt;sup&gt;2&lt;/sup&gt;. The problem associated with this measurement is its narrow swath of approximately 10.6 km width which results in limited spatial coverage.&lt;/p&gt;&lt;p&gt;A number of research works have been reported to spatially map the available XCO&lt;sub&gt;2&lt;/sub&gt; samples on a regional scale or globally in different temporal scale and spatial resolution. Kriging, a family of geostatistical interpolation method, has been a popular choice for this mapping. In our recent research, we have shown that the univariate kriging methods are not able to produce a pragmatic surface of XCO&lt;sub&gt;2&lt;/sub&gt; and require the incorporation of more covariates. We have studied the OCO-2&amp;#8217;s XCO&lt;sub&gt;2 &lt;/sub&gt;observations and mapped them on a regional scale including multiple covariates, such as Open-source Data Inventory for Anthropogenic CO2 (ODIAC) and the Emissions Database for Global Atmospheric Research (EDGAR) emission estimates and land use and land cover (LULC) information. It is observed that the inclusion of these covariates is able to produce more accurate mapping compared to their baseline alternatives.&lt;/p&gt;&lt;p&gt;However, the CO&lt;sub&gt;2&lt;/sub&gt; concentration is usually highly influenced by the transportation of the emission particles through the wind. A larger temporal measurement window may ignore its effect by assuming that the wind direction is constantly changing. However, for regional mapping of space-borne XCO&lt;sub&gt;2&lt;/sub&gt; in a time instance, it is essential to model. This work has developed a novel multivariate kriging-based framework to map OCO-2&amp;#8217;s XCO&lt;sub&gt;2 &lt;/sub&gt;measurements including Stochastic Time-Inverted Lagrangian Transport-(STILT)-based atmospheric transport modeling. This model could be coupled with the biospheric flux models and emission estimates to map their local scale distributions.&lt;/p&gt;&lt;p&gt;In this framework, every unmeasured location that is required to be estimated is considered as the receptor point in the STILT simulation. The emission particles are tracked backward in time from each of these receptor points to simulate possible routes from their upstream locations. A footprint map is then generated which is regarded as the influence of other points to the receptor point in the whole study region. The footprint map, being combined with the emission estimates, will produce a prior CO&lt;sub&gt;2 &lt;/sub&gt;concentration map. This STILT-generated prior concentration map is inserted into the multivariate kriging framework. The output map, i.e., the interpolated XCO&lt;sub&gt;2&lt;/sub&gt; surface is more pragmatic to include the influence of atmospheric transport for the prediction of XCO&lt;sub&gt;2&lt;/sub&gt;. The accuracy of the framework is proven by comparing the estimated data with ground-based measurements. This work is one of the initial attempts to generate a Level-3 XCO&lt;sub&gt;2&lt;/sub&gt; surface on a local scale by combining STILT with a multivariate kriging method.&lt;/p&gt;

scholarly journals Characterizing biospheric carbon balance using CO<sub>2</sub> observations from the OCO-2 satellite

2017 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak ◽  
Vineet Yadav ◽  
Jovan M. Tadic
Keyword(s):  
Carbon Balance ◽  
Atmospheric Transport ◽  
Synthetic Data ◽  
Initial Step ◽  
Co2 Fluxes ◽  
Top Down ◽  
Future Improvement ◽  
The Individual ◽  
Hemisphere Winter ◽  
Atmospheric Transport Modeling

Abstract. NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite launched in summer of 2014. Its observations could allow scientists to constrain CO2 fluxes across regions or continents that were previously difficult to monitor. This study explores an initial step toward that goal; we evaluate the extent to which current OCO-2 observations can detect patterns in biospheric CO2 fluxes and constrain monthly CO2 budgets. Our goal is to guide top-down, inverse modeling studies and identify areas for future improvement. We find that uncertainties and biases in the individual OCO-2 observations are comparable to the atmospheric signal from biospheric fluxes, particularly during northern hemisphere winter when biospheric fluxes are small. A series of top-down experiments indicate how these errors affect our ability to constrain monthly biospheric CO2 budgets. We are able to constrain budgets for between two and four global regions using OCO-2 observations, depending on the month, and we can constrain CO2 budgets at the regional level (i.e., smaller than seven global biomes) in only a handful of cases (16 % of all regions and months). The potential of the OCO-2 observations, however, is greater than these results might imply. A set of synthetic data experiments suggests that observation or retrieval errors have a salient effect. Advances in retrieval algorithms and to a lesser extent atmospheric transport modeling will improve the results. In the interim, top-down studies that use current satellite observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions.

Modelling of the Atmospheric Transport and Deposition of Ammonia at a National and Regional Scale

2009 ◽  
pp. 301-358 ◽  
Author(s):  
Addo van Pul ◽  
Ole Hertel ◽  
Camilla Geels ◽  
Anthony J. Dore ◽  
Massimo Vieno ◽  
...  
Keyword(s):  
Atmospheric Transport ◽  
Regional Scale

scholarly journals Abundance and Sources of Atmospheric Halocarbons in the Eastern Mediterranean

2017 ◽  
Author(s):  
Fabian Schoenenberger ◽  
Stephan Henne ◽  
Matthias Hill ◽  
Martin K. Vollmer ◽  
Giorgos Kouvarakis ◽  
...  
Keyword(s):  
Western Europe ◽  
Atmospheric Transport ◽  
Eastern Mediterranean ◽  
Stratospheric Ozone ◽  
Regional Scale ◽  
Top Down ◽  
Inversion Domain ◽  
Hfc 134A ◽  
Wide Range ◽  
Hfc 152A

Abstract. A wide range of anthropogenic halocarbons is released to the atmosphere, contributing to stratospheric ozone depletion and global warming. Using measurements of atmospheric abundances for the estimation of halocarbon emissions on the global and regional scale has become an important top-down tool for emission validation in the recent past, but many populated and developing areas of the world are only poorly covered by the existing atmospheric halocarbon measurement network. Here we present six months of continuous halocarbon observations from Finokalia on the island of Crete in the Eastern Mediterranean. The gases measured are the hydrofluorocarbons (HFCs), HFC-134a (CH2FCF3), HFC-125 (CHF2CF3), HFC-152a (CH3CHF2) and HFC-143a (CH3CF3), and the hydrochlorofluorocarbons (HCFCs), HCFC-22 (CHClF2) and HCFC-142b (CH3CClF2). The Eastern Mediterranean is home to 250 million inhabitants, consisting of a number of developed and developing countries, for which different emission regulations exist under the Kyoto and Montreal Protocols. Regional emissions of halocarbons were estimated with Lagrangian atmospheric transport simulations and a Bayesian inverse modelling system, using measurements at Finokalia in conjunction with those from Advanced Global Atmospheric Gases Experiment (AGAGE) sites at Mace Head (Ireland), Jungfraujoch (Switzerland) and Monte Cimone (Italy). Measured peak mole fractions at Finokalia showed generally smaller amplitudes for HFCs than at the European AGAGE sites, except periodic peaks of HFC-152a, indicating strong upwind sources. Higher peak mole fractions were observed for HCFCs, suggesting continued emissions from nearby developing regions such as Egypt and the Middle East. For 2013, the Eastern Mediterranean inverse emission estimates for the four analysed HFCs and the two HCFCs were 14.7 (6.7–23.3) Tg CO2eq yr-1 and 9.7 (4.3–15.7) Tg CO2eq yr-1, respectively. These emissions contributed 17.3 % (7.9–27.4 %) and 53 % (23.5–86%) to the total inversion domain, which covers the Eastern Mediterranean as well as Central and Western Europe. Greek bottom-up HFC emissions reported to the UNFCCC were much smaller than our top-down estimates, whereas for Turkey our estimates agreed with UNFCCC-reported values for HFC-125 and HFC-143a, but were much and slightly smaller for HFC-134a and HFC-152a, respectively. Sensitivity estimates suggest an improvement of the a posteriori emission estimates, i.e. a reduction of the uncertainties by 40–80 %, compared to an inversion using only the existing Central European AGAGE observations.

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Comparing atmospheric transport models for future regional inversions over Europe – Part 1: mapping the atmospheric CO<sub>2</sub> signals

2007 ◽  
Vol 7 (13) ◽  
pp. 3461-3479 ◽  
Author(s):  
C. Geels ◽  
M. Gloor ◽  
P. Ciais ◽  
P. Bousquet ◽  
P. Peylin ◽  
...  
Keyword(s):  
High Resolution ◽  
High Altitude ◽  
Large Scale ◽  
Fossil Fuel ◽  
Atmospheric Transport ◽  
Inverse Methods ◽  
Spatiotemporal Variability ◽  
Transport Models ◽  
Sources And Sinks ◽  
Low Altitude

Abstract. The CO2 source and sink distribution across Europe can be estimated in principle through inverse methods by combining CO2 observations and atmospheric transport models. Uncertainties of such estimates are mainly due to insufficient spatiotemporal coverage of CO2 observations and biases of the models. In order to assess the biases related to the use of different models the CO2 concentration field over Europe has been simulated with five different Eulerian atmospheric transport models as part of the EU-funded AEROCARB project, which has the main goal to estimate the carbon balance of Europe. In contrast to previous comparisons, here both global coarse-resolution and regional higher-resolution models are included. Continuous CO2 observations from continental, coastal and mountain sites as well as flasks sampled on aircrafts are used to evaluate the models' ability to capture the spatiotemporal variability and distribution of lower troposphere CO2 across Europe. 14CO2 is used in addition to evaluate separately fossil fuel signal predictions. The simulated concentrations show a large range of variation, with up to ~10 ppm higher surface concentrations over Western and Central Europe in the regional models with highest (mesoscale) spatial resolution. The simulation – data comparison reveals that generally high-resolution models are more successful than coarse models in capturing the amplitude and phasing of the observed short-term variability. At high-altitude stations the magnitude of the differences between observations and models and in between models is less pronounced, but the timing of the diurnal cycle is not well captured by the models. The data comparisons show also that the timing of the observed variability on hourly to daily time scales at low-altitude stations is generally well captured by all models. However, the amplitude of the variability tends to be underestimated. While daytime values are quite well predicted, nighttime values are generally underpredicted. This is a reflection of the different mixing regimes during day and night combined with different vertical resolution between models. In line with this finding, the agreement among models is increased when sampling in the afternoon hours only and when sampling the mixed portion of the PBL, which amounts to sampling at a few hundred meters above ground. The main recommendations resulting from the study for constraining land carbon sources and sinks using high-resolution concentration data and state-of-the art transport models through inverse methods are given in the following: 1) Low altitude stations are presently preferable in inverse studies. If high altitude stations are used then the model level that represents the specific sites should be applied, 2) at low altitude sites only the afternoon values of concentrations can be represented sufficiently well by current models and therefore afternoon values are more appropriate for constraining large-scale sources and sinks in combination with transport models, 3) even when using only afternoon values it is clear that data sampled several hundred meters above ground can be represented substantially more robustly in models than surface station records, which emphasize the use of tower data in inverse studies and finally 4) traditional large scale transport models seem not sufficient to resolve fine-scale features associated with fossil fuel emissions, as well as larger-scale features like the concentration distribution above the south-western Europe. It is therefore recommended to use higher resolution models for interpretation of continental data in future studies.

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