scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2021 ◽  
Vol 14 (6) ◽  
pp. 3383-3406
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Model Errors ◽  
Inversion Algorithm ◽  
In Situ Data ◽  
Increasing Demand ◽  
Modular Inversion

Abstract. Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.

scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2019 ◽  
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Inverse Modelling ◽  
Current Status ◽  
Model Errors ◽  
Inversion Algorithm ◽  
New Developments ◽  
In Situ Data ◽  
Modular Inversion

Abstract. Atmospheric inversions are commonly used for estimating large-scale (continental to regional) net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. Recently, there has been an increasing demand from stakeholders for robust estimates of greenhouse gases at country-scale (or higher) resolution, in particular in the framework of the Paris agreement. This increase in resolution is in theory enabled by the growing availability of observations from surface in-situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the process-based (bottom-up) modelling community (vegetation models, fossil fuel emission inventories). This, however, calls for new developments in the inverse modelling systems, mainly in terms of diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency, We have developed the Lund University Modular Inversion Algorithm (LUMIA) as a tool to address some of these new developments. LUMIA is meant to be a platform for inverse modelling developments at Lund University. It aims at being a flexible, yet simple and easy to maintain set of tools that modellers can combine to build inverse modelling experiments. It is in particular designed to be transport model agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. Here, we briefly describe the motivations for developing LUMIA as well as the underlying development principles, current status and future prospects. We present a first LUMIA inversion setup for a regional CO2 inversions over Europe, based on a new coupling between the Lagrangian FLEXPART (high resolution foreground transport) and the global coarse resolution TM5 transport models, using in-situ data from surface and tall tower observation sites.

scholarly journals Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM – FLEXPART coupled transport model and its adjoint

2020 ◽  
Author(s):  
Shamil Maksyutov ◽  
Tomohiro Oda ◽  
Makoto Saito ◽  
Rajesh Janardanan ◽  
Dmitry Belikov ◽  
...  
Keyword(s):  
High Resolution ◽  
Transport Model ◽  
Dispersion Model ◽  
Surface Flux ◽  
Particle Dispersion ◽  
Anthropogenic Emissions ◽  
Coupled Transport ◽  
Tracer Transport ◽  
Flux Corrections

Abstract. We developed a high-resolution surface flux inversion system based on the global Lagrangian–Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve best fit to atmospheric CO2 data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO2 emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO2 observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator is being implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in-situ CO2 data included in the Observation Package (ObsPack) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO2 fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for terrestrial biosphere was scaled proportionally to the monthly mean Gross Primary Production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO2 concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO2 flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals.

scholarly journals Technical note: A high-resolution inverse modelling technique for estimating surface CO<sub>2</sub> fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint

2021 ◽  
Vol 21 (2) ◽  
pp. 1245-1266
Author(s):  
Shamil Maksyutov ◽  
Tomohiro Oda ◽  
Makoto Saito ◽  
Rajesh Janardanan ◽  
Dmitry Belikov ◽  
...  
Keyword(s):  
High Resolution ◽  
Transport Model ◽  
Dispersion Model ◽  
Surface Flux ◽  
Particle Dispersion ◽  
Anthropogenic Emissions ◽  
Coupled Transport ◽  
Tracer Transport ◽  
Flux Corrections

Abstract. We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.

scholarly journals Inversion of long-lived trace gas emissions using combined Eulerian and Lagrangian chemical transport models

2011 ◽  
Vol 11 (18) ◽  
pp. 9887-9898 ◽  
Author(s):  
M. Rigby ◽  
A. J. Manning ◽  
R. G. Prinn
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Transport Model ◽  
Initial Conditions ◽  
Dispersion Model ◽  
Chemical Transport ◽  
Particle Dispersion ◽  
Global Network ◽  
Lagrangian Model ◽  
Chemical Transport Model

Abstract. We present a method for estimating emissions of long-lived trace gases from a sparse global network of high-frequency observatories, using both a global Eulerian chemical transport model and Lagrangian particle dispersion model. Emissions are derived in a single step after determining sensitivities of the observations to initial conditions, the high-resolution emissions field close to observation points, and larger regions further from the measurements. This method has the several advantages over inversions using one type of model alone, in that: high-resolution simulations can be carried out in limited domains close to the measurement sites, with lower resolution being used further from them; the influence of errors due to aggregation of emissions close to the measurement sites can be minimized; assumptions about boundary conditions to the Lagrangian model do not need to be made, since the entire emissions field is estimated; any combination of appropriate models can be used, with no code modification. Because the sensitivity to the entire emissions field is derived, the estimation can be carried out using traditional statistical methods without the need for multiple steps in the inversion. We demonstrate the utility of this approach by determining global SF6 emissions using measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) between 2007 and 2009. The global total and large-scale patterns of the derived emissions agree well with previous studies, whilst allowing emissions to be determined at higher resolution than has previously been possible, and improving the agreement between the modeled and observed mole fractions at some sites.

scholarly journals Boundary layer and free-tropospheric dimethyl sulfide in the Arctic spring and summer

2017 ◽  
Vol 17 (14) ◽  
pp. 8757-8770 ◽  
Author(s):  
Roghayeh Ghahremaninezhad ◽  
Ann-Lise Norman ◽  
Betty Croft ◽  
Randall V. Martin ◽  
Jeffrey R. Pierce ◽  
...  
Keyword(s):  
Dimethyl Sulfide ◽  
Transport Model ◽  
Dispersion Model ◽  
Open Water ◽  
Particle Dispersion ◽  
The Arctic ◽  
Chemical Transport Model ◽  
Model Simulations ◽  
Baffin Bay ◽  
Mixing Ratios

Abstract. Vertical distributions of atmospheric dimethyl sulfide (DMS(g)) were sampled aboard the research aircraft Polar 6 near Lancaster Sound, Nunavut, Canada, in July 2014 and on pan-Arctic flights in April 2015 that started from Longyearbyen, Spitzbergen, and passed through Alert and Eureka, Nunavut, and Inuvik, Northwest Territories. Larger mean DMS(g) mixing ratios were present during April 2015 (campaign mean of 116  ±  8 pptv) compared to July 2014 (campaign mean of 20  ±  6 pptv). During July 2014, the largest mixing ratios were found near the surface over the ice edge and open water. DMS(g) mixing ratios decreased with altitude up to about 3 km. During April 2015, profiles of DMS(g) were more uniform with height and some profiles showed an increase with altitude. DMS reached as high as 100 pptv near 2500 m. Relative to the observation averages, GEOS-Chem (www.geos-chem.org) chemical transport model simulations were higher during July and lower during April. Based on the simulations, more than 90 % of the July DMS(g) below 2 km and more than 90 % of the April DMS(g) originated from Arctic seawater (north of 66° N). During April, 60 % of the DMS(g), between 500 and 3000 m originated from Arctic seawater. During July 2014, FLEXPART (FLEXible PARTicle dispersion model) simulations locate the sampled air mass over Baffin Bay and the Canadian Arctic Archipelago 4 days back from the observations. During April 2015, the locations of the air masses 4 days back from sampling were varied: Baffin Bay/Canadian Archipelago, the Arctic Ocean, Greenland and the Pacific Ocean. Our results highlight the role of open water below the flight as the source of DMS(g) during July 2014 and the influence of long-range transport (LRT) of DMS(g) from further afield in the Arctic above 2500 m during April 2015.

scholarly journals A framework for comparing remotely sensed and in-situ CO<sub>2</sub> concentrations

2008 ◽  
Vol 8 (1) ◽  
pp. 1549-1588 ◽  
Author(s):  
R. Macatangay ◽  
T. Warneke ◽  
C. Gerbig ◽  
S. Körner ◽  
R. Ahmadov ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Transport Model ◽  
Remotely Sensed ◽  
Infrared Spectrometry ◽  
Remotely Sensed Data ◽  
Solar Absorption ◽  
Mixing Ratios ◽  
In Situ Data ◽  
Global Calibration

Abstract. A framework that allows validating CO2 column averaged volume mixing ratios (VMRs) retrieved from ground-based solar absorption measurements using Fourier transform infrared spectrometry (FTS) against measurements made in-situ (such as from aircrafts and tall towers) has been developed. Since in-situ measurements are done frequently and at high accuracy on the global calibration scale, linking this scale with FTS total column retrievals ultimately provides a calibration scale for remote sensing. FTS, tower and aircraft data were analyzed from measurements during the CarboEurope Regional Experiment Strategy (CERES) from May to June 2005 in Biscarrosse, France. Carbon dioxide VMRs from the MetAir Dimona aircraft, the TM3 global transport model and Observations of the Middle Stratosphere (OMS) balloon based experiments were combined and integrated to compare with FTS measurements. The comparison agrees fairly well with differences resulting from the spatial variability of CO2 around the FTS as measured by the aircraft. Additionally, the Stochastic Time Inverted Lagrangian Transport (STILT) model served as a "transfer standard" between the in-situ data measured at a co-located tower and the remotely sensed data from the FTS. The variability of carbon dioxide VMRs was modeled well by STILT with differences coming partly from uncertainties in the spatial variation of carbon dioxide.

scholarly journals Study of the footprints of short-term variation in XCO<sub>2</sub> observed by TCCON sites using NIES and FLEXPART atmospheric transport models

2016 ◽  
Author(s):  
D. A. Belikov ◽  
S. Maksyutov ◽  
A. Ganshin ◽  
R. Zhuravlev ◽  
N. M. Deutscher ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Temporal Variations ◽  
Particle Dispersion ◽  
Bias Reduction ◽  
Total Carbon ◽  
Short Term

Abstract. The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers (FTS) that record near-infrared (NIR) spectra of the Sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fraction (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle) Lagrangian Particle Dispersion Model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future, and possible TCCON sites. We propose a footprint-based method for the colocation of satellite and TCCON XCO2 observations, and estimate the performance of the method using the NIES model and five GOSAT XCO2 product datasets. Comparison of the proposed approach with a standard geographic method shows higher number of colocation points and average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and La Réunion sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasizes that the colocation is sensitive to local meteorological conditions and flux distributions.

Global Methane Emissions Through an Isotopic Lens

2020 ◽  
Author(s):  
Alice Drinkwater ◽  
Tim Arnold ◽  
Paul Palmer
Keyword(s):  
Transport Model ◽  
Global Climate ◽  
Agricultural Practices ◽  
Isotope Ratios ◽  
Atmospheric Methane ◽  
Source Attribution ◽  
Chemical Transport Model ◽  
Magnitude Distribution ◽  
In Situ Data

&lt;p&gt;Changes in atmospheric methane (CH&lt;sub&gt;4&lt;/sub&gt;) are mainly driven by natural, anthropogenic and pyrogenic emissions and oxidation by OH.&lt;/p&gt;&lt;p&gt;There is no consensus about the underlying explanations about hemispheric-scale changes in atmospheric methane (CH&lt;sub&gt;4&lt;/sub&gt;). This is partly due to sparse data that do not exclusively identify individual changes in surface emissions and surface and atmospheric losses of CH&lt;sub&gt;4&lt;/sub&gt;. This challenge represents a major scientific weakness in our understanding of this potent greenhouse gas, with implications for meeting global climate policy obligations. &amp;#160;A confounding challenge is that the regional importance of individual emission sources change with time due to, for example, innovations in agricultural practices, climate-sensitive wetlands, and political decisions associated with climate friendlier transitional fuels. &amp;#160;&lt;/p&gt;&lt;p&gt;&lt;br&gt;Here we use bulk isotope ratios &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C and &amp;#948;D of CH&lt;sub&gt;4&lt;/sub&gt; that have been previously shown to provide effective constraints on source apportionment: different CH&lt;sub&gt;4&lt;/sub&gt; sources have characteristic isotope ratios. One of the key challenges associated with using these data is that region-specific isotope ratios change with time due to varying source prevalance, in addition to source signatures having inherent uncertainties. We use the GEOS-Chem global 3-D chemical transport model to describe the spatial and temporal isotopic behaviour of atmospheric CH&lt;sub&gt;4&lt;/sub&gt;. We develop a Maximum A-Posteriori inverse method to simultaneously infer time dependent CH&lt;sub&gt;4&lt;/sub&gt; emissions and isotope ratios from in situ data.&amp;#160;&lt;/p&gt;&lt;p&gt;We will report the magnitude, distribution and source attribution of CH&lt;sub&gt;4&lt;/sub&gt; emissions from 2004 to 2017, inferred from in situ measurements of total atmospheric CH&lt;sub&gt;4&lt;/sub&gt; mole fraction and the corresponding measurements of &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C and &amp;#948;D. We will compare our results with previous studies.&lt;/p&gt;

scholarly journals A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations

2011 ◽  
Vol 4 (3) ◽  
pp. 2047-2080 ◽  
Author(s):  
A. Ganshin ◽  
T. Oda ◽  
M. Saito ◽  
S. Maksyutov ◽  
V. Valsala ◽  
...  
Keyword(s):  
High Resolution ◽  
High Frequency ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Lagrangian Model ◽  
Co2 Concentrations ◽  
Atmospheric Co2 Concentrations

Abstract. We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e., a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

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