Prior Flux Research Articles

Evaluation of MOPITT Version 7 joint TIR–NIR X<sub>CO</sub> retrievals with TCCON

Abstract. Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Designing surface CO2 monitoring network to constrain the Indian land fluxes

Optimal network design, for ground-based monitoring of atmospheric mole fractions of carbon dioxide (CO2) over India to better constrain the Indian terrestrial surface fluxes, is proposed here using a Lagrangian Particle Dispersion Model FLEXPART and Bayesian inversion methods. Potential emission sensitivity from FLEXPART, prior flux uncertainties from CASA-GFED biosphere fluxes and CDIAC fossil fuel fluxes, and assumed uniform observational uncertainty of 2 ppm are used to calculate prior and posterior cost functions. A total of 73 grid cells are identified over the Indian region in 2°x2° latitude by longitude resolution assuming each cell as a potential site, ‘a-priori’. Further, the effectiveness of CO2 observations from these locations to reduce the Indian terrestrial flux uncertainty is quantified using an incremental optimization method. Based on the above objective, we have (a) ranked the existing nine stations, (b) devised a methodology to design an extended network by adding a few more potential stations to the existing stations, and (c) identified a completely new set of optimal stations for measuring atmospheric CO2 over India.The study shows that the base network of existing stations scattered over India could reduce the uncertainty of Indian terrestrial surface flux estimation in an inversion framework, at this resolution, by more than 30% during post-monsoon and winter season, and by more than 15% during other parts of the year. Addition of five new stations to the base network could reduce the uncertainty by an additional 15% for all the seasons reaching up to 45%. In the ‘new network’, twelve stations are able to achieve an equivalent flux uncertainty reduction of fourteen stations achieved by the ‘extended network’ during each season. Locations of stations are more important than the magnitude of reduction since it depends on the values of the prior and observational uncertainties. The most important and essential ‘new’ stations in the extended network are over the northeastern region and Indo-Gangetic plain, during most of the year. Only during the post-monsoon season, additional stations are required in the western peninsular region. Finally, a consolidated set for all seasons comprising seventeen members is proposed. The study highlights a major zone of CO2 ‘observational void’ that exists in potential locations near east and northeast parts of India. Immediate requirement of CO2 monitoring initiative in these areas is highly recommended.

Global atmospheric carbon monoxide budget 2000–2017 inferred from multi-species atmospheric inversions

Abstract. Atmospheric carbon monoxide (CO) concentrations have been decreasing since 2000, as observed by both satellite- and ground-based instruments, but global bottom-up emission inventories estimate increasing anthropogenic CO emissions concurrently. In this study, we use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric CO variations to its sources and sinks in order to achieve a full closure of the global CO budget during 2000–2017. Our observation constraints include satellite retrievals of the total column mole fraction of CO, formaldehyde (HCHO), and methane (CH4) that are all major components of the atmospheric CO cycle. Three inversions (i.e., 2000–2017, 2005–2017, and 2010–2017) are performed to use the observation data to the maximum extent possible as they become available and assess the consistency of inversion results to the assimilation of more trace gas species. We identify a declining trend in the global CO budget since 2000 (three inversions are broadly consistent during overlapping periods), driven by reduced anthropogenic emissions in the US and Europe (both likely from the transport sector), and in China (likely from industry and residential sectors), as well as by reduced biomass burning emissions globally, especially in equatorial Africa (associated with reduced burned areas). We show that the trends and drivers of the inversion-based CO budget are not affected by the inter-annual variation assumed for prior CO fluxes. All three inversions contradict the global bottom-up inventories in the world's top two emitters: for the sign of anthropogenic emission trends in China (e.g., here -0.8±0.5 % yr−1 since 2000, while the prior gives 1.3±0.4 % yr−1) and for the rate of anthropogenic emission increase in South Asia (e.g., here 1.0±0.6 % yr−1 since 2000, smaller than 3.5±0.4 % yr−1 in the prior inventory). The posterior model CO concentrations and trends agree well with independent ground-based observations and correct the prior model bias. The comparison of the three inversions with different observation constraints further suggests that the most complete constrained inversion that assimilates CO, HCHO, and CH4 has a good representation of the global CO budget, and therefore matches best with independent observations, while the inversion only assimilating CO tends to underestimate both the decrease in anthropogenic CO emissions and the increase in the CO chemical production. The global CO budget data from all three inversions in this study can be accessed from https://doi.org/10.6084/m9.figshare.c.4454453.v1 (Zheng et al., 2019).

The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network

Abstract. The Orbiting Carbon Observatory-2 has been on orbit since 2014, and its global coverage holds the potential to reveal new information about the carbon cycle through the use of top-down atmospheric inversion methods combined with column average CO2 retrievals. We employ a large ensemble of atmospheric inversions utilizing different transport models, data assimilation techniques, and prior flux distributions in order to quantify the satellite-informed fluxes from OCO-2 Version 7r land observations and their uncertainties at continental scales. Additionally, we use in situ measurements to provide a baseline against which to compare the satellite-constrained results. We find that within the ensemble spread, in situ observations, and satellite retrievals constrain a similar global total carbon sink of 3.7±0.5 PgC yr−1, and 1.5±0.6 PgC yr−1 for global land, for the 2015–2016 annual mean. This agreement breaks down in smaller regions, and we discuss the differences between the experiments. Of particular interest is the difference between the different assimilation constraints in the tropics, with the largest differences occurring in tropical Africa, which could be an indication of the global perturbation from the 2015–2016 El Niño. Evaluation of posterior concentrations using TCCON and aircraft observations gives some limited insight into the quality of the different assimilation constraints, but the lack of such data in the tropics inhibits our ability to make strong conclusions there.

An atmospheric inversion over the city of Cape Town: sensitivity analyses

Abstract. An atmospheric inversion was performed for the city of Cape Town for the period of March 2012 to June 2013, making use of in situ measurements of CO2 concentrations at temporary measurement sites located to the north-east and south-west of Cape Town. This paper presents results of sensitivity analyses that tested assumptions regarding the prior information and the uncertainty covariance matrices associated with the prior fluxes and with the observations. Alternative prior products were considered in the form of a carbon assessment analysis to provide biogenic fluxes and the ODIAC (Open-source Data Inventory for Anthropogenic CO2 product) fossil fuel product. These were used in place of the reference inversion's biogenic fluxes from CABLE (Community Atmosphere Biosphere Land Exchange model) and fossil fuel emissions from a bespoke inventory analysis carried out specifically for the Cape Town inversion. Our results confirmed that the inversion solution was strongly dependent on the prior information, but by using independent alternative prior products to run multiple inversions, we were able to infer limits for the true domain flux. Where the reference inversion had aggregated prior flux estimates that were made more positive by the inversion – suggesting that CABLE was overestimating the amount of CO2 biogenic uptake – the carbon assessment prior fluxes were made more negative by the inversion. As the posterior estimates tended towards the same point, we could infer that the best estimate was located somewhere between these two posterior fluxes. The inversion was shown to be sensitive to the spatial error correlation length in the biogenic fluxes – even a short correlation length – influencing the spatial distribution of the posterior fluxes, the size of the aggregated flux across the domain, and the uncertainty reduction achieved by the inversion. Taking advantage of expected spatial correlations in the fluxes is key to maximizing the use of a limited observation network. Changes to the temporal correlations in the observation errors had a very minor effect on the inversion. The control vector in the original version consisted of separate daytime and night-time weekly fluxes for fossil fuel and biogenic fluxes over a 4-week inversion period. When we considered solving for mean weekly fluxes over each 4-week period – i.e. assuming the flux remained constant over the month – larger changes to the prior fossil fuel and biogenic fluxes were possible, as well as further changes to the spatial distribution of the fluxes compared with the reference. The uncertainty reduction achieved in the estimation of the overall flux increased from 25.6 % for the reference inversion to 47.2 % for the mean weekly flux inversion. This demonstrates that if flux components that change slowly can be solved for separately in the inversion, where these fluxes are assumed to be constant over long periods of time, the posterior estimates of these fluxes substantially benefit from the additional observational constraint. In summary, estimates of Cape Town fluxes can be improved by using better and multiple prior information sources, and particularly on biogenic fluxes. Fossil fuel and biogenic fluxes should be broken down into components, building in knowledge of spatial and temporal consistency in these components into the control vector and uncertainties specified for the sources for the inversion. This would allow the limited observations to provide maximum constraint on the flux estimates.

Modelling CO<sub>2</sub> weather – why horizontal resolution matters

Abstract. Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser-resolution models. These representativeness errors need to be considered when assimilating in situ data and high-resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high-resolution CO2 simulations provided by the CAMS in real time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.

Analysis of atmospheric CH<sub>4</sub> in Canadian Arctic and estimation of the regional CH<sub>4</sub> fluxes

Abstract. The Canadian Arctic (> 60∘ N, 60–141∘ W) may undergo drastic changes if the Arctic warming trend continues. For methane (CH4), Arctic reservoirs are large and widespread, and the climate feedbacks from such changes may be potentially substantial. Current bottom-up and top-down estimates of the regional CH4 flux range widely. This study analyzes the recent observations of atmospheric CH4 from five arctic monitoring sites and presents estimates of the regional CH4 fluxes for 2012–2015. The observational data reveal sizeable synoptic summertime enhancements in the atmospheric CH4 that are distinguishable from background variations, which indicate strong regional fluxes (primarily wetland and biomass burning CH4 emissions) around Behchoko and Inuvik in the western Canadian Arctic. Three regional Bayesian inversion modelling systems with two Lagrangian particle dispersion models and three meteorological datasets are applied to estimate fluxes for the Canadian Arctic and show relatively robust results in amplitude and temporal variations across different transport models, prior fluxes, and subregion masking. The estimated mean total CH4 flux for the entire Canadian Arctic is 1.8±0.6 Tg CH4 yr−1. The flux estimate is partitioned into biomass burning of 0.3±0.1 Tg CH4 yr−1 and the remaining natural (wetland) flux of 1.5±0.5 Tg CH4 yr−1. The summer natural CH4 flux estimates clearly show inter-annual variability that is positively correlated with surface temperature anomalies. The results indicate that years with warmer summer conditions result in more wetland CH4 emissions. More data and analysis are required to statistically characterize the dependence of regional CH4 fluxes on the climate in the Arctic. These Arctic measurement sites will aid in quantifying the inter-annual variations and long-term trends in CH4 emissions in the Canadian Arctic.

Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network

Abstract. We present a method to derive atmospheric-observation-based estimates of carbon dioxide (CO2) fluxes at the national scale, demonstrated using data from a network of surface tall-tower sites across the UK and Ireland over the period 2013–2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net ecosystem exchange of CO2. Two different models, Data Assimilation Linked Ecosystem Carbon (DALEC) and Joint UK Land Environment Simulator (JULES), provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences between the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of 8±79 Tg CO2 yr−1 (DALEC prior) and 64±85 Tg CO2 yr−1 (JULES prior), where negative values represent an uptake of CO2. These biospheric CO2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates consistently indicate a greater net release of CO2 than the prior estimates, which show much more pronounced uptake in summer months.

The X-ray Variability of AGN and its Implications for Observations of Galaxy Clusters

The detection of new clusters of galaxies or the study of known clusters of galaxies in X-rays can be complicated by the presence of X-ray point sources, the majority of which will be active galactic nuclei (AGN). This can be addressed by combining observations from a high angular resolution observatory (such as Chandra) with deeper data from a more sensitive observatory that may not be able to resolve the AGN (like XMM). However, this approach is undermined if the AGN varies in flux between the epochs of the observations. To address this we measure the characteristic X-ray variability of serendipitously detected AGN in 70 pairs of Chandra observations, separated by intervals of between one month and thirteen years. After quality cuts, the full sample consists of 1511 sources, although the main analysis uses a subset of 416 sources selected on the geometric mean of their flux in the pairs of observations, which eliminates selection biases. We find a fractional variability that increases with increasing interval between observations, from about 0.25 for observations separated by tens of days up to about 0.45 for observations separated by $\sim 10$ years. As a rule of thumb, given the precise X-ray flux of a typical AGN at one epoch, its flux at a second epoch some years earlier or later can be predicted with a precision of about $60\%$ due to its variability (ignoring any statistical noise). This is larger than the characteristic variability of the population by a factor of $\sqrt{2}$ due to the uncertainty on the mean flux of the AGN due to a single prior measurement. The precision can thus be improved with multiple prior flux measurements (reducing the $\sqrt{2}$ factor), or by reducing the interval between observations to reduce the characteristic variability.

Global atmospheric CO2 inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate.

We have compared a suite of recent global CO2 atmospheric inversion results to independent airborne observations and to each other, to assess their dependence on differences in northern extratropical (NET) vertical transport and to identify some of the drivers of model spread. We evaluate posterior CO2 concentration profiles against observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) aircraft campaigns over the mid-Pacific in 2009–2011. Although the models differ in inverse approaches, assimilated observations, prior fluxes, and transport models, their broad latitudinal separation of land fluxes has converged significantly since the Atmospheric Carbon Cycle Inversion Intercomparison (TransCom 3) and the REgional Carbon Cycle Assessment and Processes (RECCAP) projects, with model spread reduced by 80% since TransCom 3 and 70% since RECCAP. Most modeled CO2 fields agree reasonably well with the HIPPO observations, specifically for the annual mean vertical gradients in the Northern Hemisphere. Northern Hemisphere vertical mixing no longer appears to be a dominant driver of northern versus tropical (T) annual flux differences. Our newer suite of models still gives northern extratropical land uptake that is modest relative to previous estimates (Gurney et al., 2002; Peylin et al., 2013) and near-neutral tropical land uptake for 2009–2011. Given estimates of emissions from deforestation, this implies a continued uptake in intact tropical forests that is strong relative to historical estimates (Gurney et al., 2002; Peylin et al., 2013). The results from these models for other time periods (2004–2014, 2001–2004, 1992–1996) and reevaluation of the TransCom 3 Level 2 and RECCAP results confirm that tropical land carbon fluxes including deforestation have been near neutral for several decades. However, models still have large disagreements on ocean–land partitioning. The fossil fuel (FF) and the atmospheric growth rate terms have been thought to be the best-known terms in the global carbon budget, but we show that they currently limit our ability to assess regional-scale terrestrial fluxes and ocean–land partitioning from the model ensemble.

Grid Voltage Sensorless Model-Based Predictive Power Control of PWM Rectifiers Based on Sliding Mode Virtual Flux Observer

In this paper, a grid voltage sensorless model predictive control is proposed based on a sliding mode virtual flux observer (SMVFO). The proposed SMVFO shows good inherent filtering capacity, and thus there is no high-frequency chattering problem. In addition, the proposed SMVFO is designed based on the closed-loop current estimation. Not only is DC-drift issue solved but also dynamic response is enhanced when compared with the prior open-loop virtual flux observer. To verify the effectiveness of the presented SMVFO, it is further integrated into finite control set-model predictive control (FCS-MPC) for pulse width modulator (PWM) rectifiers. The whole control algorithm features simplicity and improved cost-effectiveness due to the absence of modulator and grid voltage sensors. As the SMVFO can predict current at the next sampling instant while estimating virtual flux accurately, the proposed SMVFO assisted FCS-MPC is comparable to its counterpart using measured grid voltage. The simulation and experimental tests were carried out on a two-level voltage source PWM rectifier to validate the effectiveness of the proposed method.

Attribution of recent increases in atmospheric methane through 3-D inverse modelling

Abstract. The atmospheric methane (CH4) growth rate has varied considerably in recent decades. Unexplained renewed growth after 2006 followed 7 years of stagnation and coincided with an isotopic trend toward CH4 more depleted in 13C, suggesting changes in sources and/or sinks. Using surface observations of both CH4 and the relative change of isotopologue ratio (δ13CH4) to constrain a global 3-D chemical transport model (CTM), we have performed a synthesis inversion for source and sink attribution. Our method extends on previous studies by providing monthly and regional attribution of emissions from six different sectors and changes in atmospheric sinks for the extended 2003–2015 period. Regional evaluation of the model CH4 tracer with independent column observations from the Greenhouse Gases Observing Satellite (GOSAT) shows improved performance when using posterior fluxes (R=0.94–0.96, RMSE =8.3–16.5 ppb), relative to prior fluxes (R=0.60–0.92, RMSE =48.6–64.6 ppb). Further independent validation with data from the Total Carbon Column Observing Network (TCCON) shows a similar improvement in the posterior fluxes (R=0.87, RMSE =18.8 ppb) compared to the prior fluxes (R=0.69, RMSE =55.9 ppb). Based on these improved posterior fluxes, the inversion results suggest the most likely cause of the renewed methane growth is a post-2007 1.8±0.4 % decrease in mean OH, a 12.9±2.7 % increase in energy sector emissions, mainly from Africa–Middle East and southern Asia–Oceania, and a 2.6±1.8 % increase in wetland emissions, mainly from northern Eurasia. The posterior wetland flux increases are in general agreement with bottom-up estimates, but the energy sector growth is greater than estimated by bottom-up methods. The model results are consistent across a range of sensitivity analyses. When forced to assume a constant (annually repeating) OH distribution, the inversion requires a greater increase in energy sector (13.6±2.7 %) and wetland (3.6±1.8 %) emissions and an 11.5±3.8 % decrease in biomass burning emissions. Assuming no prior trend in sources and sinks slightly reduces the posterior growth rate in energy sector and wetland emissions and further increases the magnitude of the negative OH trend. We find that possible tropospheric Cl variations do not influence δ13CH4 and CH4 trends, although we suggest further work on Cl variability is required to fully diagnose this contribution. While the study provides quantitative insight into possible emissions variations which may explain the observed trends, uncertainty in prior source and sink estimates and a paucity of δ13CH4 observations limit the robustness of the posterior estimates.

CTDAS-Lagrange v1.0: a high-resolution data assimilation system for regional carbon dioxide observations

Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model–data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC yr−1. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91±1.10 PgC yr−1) and CarbonTracker Europe (version CTE2016, -0.91±0.31 PgC yr−1). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.

A comparison of posterior atmospheric CO<sub>2</sub> adjustments obtained from in situ and GOSAT constrained flux inversions

Abstract. Posterior fluxes obtained from inverse modelling are difficult to verify because there is no dense network of flux measurements available to evaluate estimates against. Here we present a new diagnostic to evaluate structures in posterior fluxes. First, we simulate the change in atmospheric CO2 fields between posterior and prior fluxes, referred to as the posterior atmospheric adjustments due to updated fluxes (PAAFs). Second, we calculate the uncertainty in atmospheric CO2 fields due solely to uncertainty in the meteorological fields, referred to as the posterior atmospheric adjustments due to imperfect meteorology (PAAMs). We argue that PAAF can only be considered robust if it exceeds PAAM, that is, the changes in atmospheric CO2 between the posterior and prior fluxes should at least exceed atmospheric CO2 changes arising from imperfect meteorology. This diagnostic is applied to two CO2 flux inversions: one which assimilates observations from the in situ CO2 network and the other which assimilates observations from the Greenhouse Gases Observing SATellite (GOSAT). On the global scale, PAAF in the troposphere reflects northern extratropical fluxes, whereas stratospheric adjustments primarily reflect tropical fluxes. In general, larger spatiotemporal variations in PAAF are obtained for the GOSAT inversion than for the in situ inversion. Zonal standard deviations of the PAAF exceed the PAAM through most of the year when GOSAT observations are used, but the minimum value is exceeded only in boreal summer when in situ observations are used. Zonal spatial structures in GOSAT-based PAAF exceed PAAM throughout the year in the tropics and through most of the year in the northern extratropics, suggesting GOSAT flux inversions can constrain zonal asymmetries in fluxes. However, we cannot discount the possibility that these structures are influenced by biases in GOSAT retrievals. Verification of such spatial structures will require a dense network of independent observations. Because PAAF depends on the choice of prior fluxes, the comparison with PAAM is system dependent and thus can be used to monitor a given assimilation system's behaviour.

Attributing the Carbon Cycle Impacts of CMIP5 Historical and Future Land Use and Land Cover Change in the Community Earth System Model (CESM1)

AbstractHistorical global land use and land cover change (LULCC) emissions of 160 PgC represent a third of all human CO2 emissions from 1850 to 2010. Future land management decisions will have large impacts on the global carbon cycle, with scenarios ranging from continued deforestation and wood harvest to mitigation scenarios that reduce total emissions through afforestation and conversion to bioenergy. Here we present a systematic assessment of Community Earth System Model (CESM1) Coupled Model Intercomparison Project phase 5 (CMIP5) historic and projection simulations, with and without LULCC. For the historical period, CESM produced a Net LULCC flux of 123 PgC to the atmosphere, removing −130 PgC from ecosystems while increasing wood product pools by 7 PgC. Historical LULCC fluxes were equally divided between conversion and wood harvest fluxes. For the Representative Concentration Pathway (RCP) 4.5 afforestation scenario CESM produced a Net LULCC flux of 53 PgC to the atmosphere, removing −58 PgC out of the ecosystem and increasing wood product pools of 5 PgC. The afforestation offsets the Direct LULCC flux of 153 PgC with a negative Indirect LULCC flux of −94 PgC. For the RCP 8.5 high LULCC scenario, CESM produced a Net LULCC flux of 211 PgC, removing −227 PgC from ecosystems and increasing wood product pools of 15 PgC. The LULCC of the future RCP scenarios was dominated by wood harvest fluxes, which is a process that was not included in many of the CMIP5 models. The analysis framework also allowed the attribution of Indirect and Prior LULCC fluxes that offset the Direct LULCC fluxes in many cases.

Estimates of CO<sub>2</sub> fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling

Abstract. We present a city-scale inversion over Cape Town, South Africa. Measurement sites for atmospheric CO2 concentrations were installed at Robben Island and Hangklip lighthouses, located downwind and upwind of the metropolis. Prior estimates of the fossil fuel fluxes were obtained from a bespoke inventory analysis where emissions were spatially and temporally disaggregated and uncertainty estimates determined by means of error propagation techniques. Net ecosystem exchange (NEE) fluxes from biogenic processes were obtained from the land atmosphere exchange model CABLE (Community Atmosphere Biosphere Land Exchange). Uncertainty estimates were based on the estimates of net primary productivity. CABLE was dynamically coupled to the regional climate model CCAM (Conformal Cubic Atmospheric Model), which provided the climate inputs required to drive the Lagrangian particle dispersion model. The Bayesian inversion framework included a control vector where fossil fuel and NEE fluxes were solved for separately.Due to the large prior uncertainty prescribed to the NEE fluxes, the current inversion framework was unable to adequately distinguish between the fossil fuel and NEE fluxes, but the inversion was able to obtain improved estimates of the total fluxes within pixels and across the domain. The median of the uncertainty reductions of the total weekly flux estimates for the inversion domain of Cape Town was 28 %, but reach as high as 50 %. At the pixel level, uncertainty reductions of the total weekly flux reached up to 98 %, but these large uncertainty reductions were for NEE-dominated pixels. Improved corrections to the fossil fuel fluxes would be possible if the uncertainty around the prior NEE fluxes could be reduced. In order for this inversion framework to be operationalised for monitoring, reporting, and verification (MRV) of emissions from Cape Town, the NEE component of the CO2 budget needs to be better understood. Additional measurements of Δ14C and δ13C isotope measurements would be a beneficial component of an atmospheric monitoring programme aimed at MRV of CO2 for any city which has significant biogenic influence, allowing improved separation of contributions from NEE and fossil fuel fluxes to the observed CO2 concentration.

Comparison of the genetic algorithm and incremental optimisation routines for a Bayesian inverse modelling based network design

The design of an optimal network of atmospheric monitoring stations for the observation of carbon dioxide (CO2) concentrations can be obtained by applying an optimisation algorithm to a cost function based on minimising posterior uncertainty in the CO2 fluxes obtained from a Bayesian inverse modelling solution. Two candidate optimisation methods assessed were the evolutionary algorithm: the genetic algorithm (GA), and the deterministic algorithm: the incremental optimisation (IO) routine.This paper assessed the ability of the IO routine in comparison to the more computationally demanding GA routine to optimise the placement of a five-member network of CO2 monitoring sites located in South Africa. The comparison considered the reduction in uncertainty of the overall flux estimate, the spatial similarity of solutions, and computational requirements. Although the IO routine failed to find the solution with the global maximum uncertainty reduction, the resulting solution had only fractionally lower uncertainty reduction compared with the GA, and at only a quarter of the computational resources used by the lowest specified GA algorithm. The GA solution set showed more inconsistency if the number of iterations or population size was small, and more so for a complex prior flux covariance matrix. If the GA completed with a sub-optimal solution, these solutions were similar in fitness to the best available solution.Two additional scenarios were considered, with the objective of creating circumstances where the GA may outperform the IO. The first scenario considered an established network, where the optimisation was required to add an additional five stations to an existing five-member network. In the second scenario the optimisation was based only on the uncertainty reduction within a subregion of the domain. The GA was able to find a better solution than the IO under both scenarios, but with only a marginal improvement in the uncertainty reduction. These results suggest that the best use of resources for the network design problem would be spent in improvement of the prior estimates of the flux uncertainties rather than investing these resources in running a complex evolutionary optimisation algorithm.The authors recommend that, if time and computational resources allow, that multiple optimisation techniques should be used as a part of a comprehensive suite of sensitivity tests when performing such an optimisation exercise. This will provide a selection of best solutions which could be ranked based on their utility and practicality.

Technical Note: Atmospheric CO<sub>2</sub> inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation

Abstract. Atmospheric inversions are widely used in the optimization of surface carbon fluxes on a regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not directly reflect the true flux uncertainties but is used to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model–data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, in which the flux constraint is derived following the same residual analysis but applied to the model–model mismatch. The synthetic study showed a quite good agreement between posterior and true fluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the known truth by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)

Abstract. The Canadian Land Surface Scheme and the Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component in the family of Canadian Earth system models (CanESMs). Here, CLASS-CTEM is coupled to Environment and Climate Change Canada (ECCC)'s weather and greenhouse gas forecast model (GEM-MACH-GHG) to consistently model atmosphere–land exchange of CO2. The coupling between the land and the atmospheric transport model ensures consistency between meteorological forcing of CO2 fluxes and CO2 transport. The procedure used to spin up carbon pools for CLASS-CTEM for multi-decadal simulations needed to be significantly altered to deal with the limited availability of consistent meteorological information from a constantly changing operational environment in the GEM-MACH-GHG model. Despite the limitations in the spin-up procedure, the simulated fluxes obtained by driving the CLASS-CTEM model with meteorological forcing from GEM-MACH-GHG were comparable to those obtained from CLASS-CTEM when it is driven with standard meteorological forcing from the Climate Research Unit (CRU) combined with reanalysis fields from the National Centers for Environmental Prediction (NCEP) to form CRU-NCEP dataset. This is due to the similarity of the two meteorological datasets in terms of temperature and radiation. However, notable discrepancies in the seasonal variation and spatial patterns of precipitation estimates, especially in the tropics, were reflected in the estimated carbon fluxes, as they significantly affected the magnitude of the vegetation productivity and, to a lesser extent, the seasonal variations in carbon fluxes. Nevertheless, the simulated fluxes based on the meteorological forcing from the GEM-MACH-GHG model are consistent to some extent with other estimates from bottom-up or top-down approaches. Indeed, when simulated fluxes obtained by driving the CLASS-CTEM model with meteorological data from the GEM-MACH-GHG model are used as prior estimates for an atmospheric CO2 inversion analysis using the adjoint of the GEOS-Chem model, the retrieved CO2 flux estimates are comparable to those obtained from other systems in terms of the global budget and the total flux estimates for the northern extratropical regions, which have good observational coverage. In data-poor regions, as expected, differences in the retrieved fluxes due to the prior fluxes become apparent. Coupling CLASS-CTEM into the Environment Canada Carbon Assimilation System (EC-CAS) is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints.

Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

The Indianapolis Flux Experiment aims to utilize a variety of atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic greenhouse gas emissions from an urban environment. We present a Bayesian inversion system solving for fossil fuel and biogenic CO2 fluxes over the city of Indianapolis, IN. Both components were described at 1 km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments, we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies and errors. We also test the impacts of flux error structures, biogenic CO2 fluxes and atmospheric transport errors on estimating fossil fuel CO2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements produce significant improvement in fossil fuel CO2 flux estimates. Systematic measurement errors of 1 ppm produce significantly biased inverse solutions, degrading the accuracy of retrieved emissions by about 1 µmol m–2 s–1 compared to the spatially averaged anthropogenic CO2 emissions of 5 µmol m–2 s–1. The presence of biogenic CO2 fluxes (similar magnitude to the anthropogenic fluxes) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO2 measurements with 1 ppm random error in addition to total CO2 measurements can partially compensate for the interference from biogenic CO2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. Finally, the precision of the inverse flux estimate is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO2 flux estimates.