Search

The trajectories of soil carbon (C) in the changing climate are of utmost importance, as soil carbon is a substantial carbon storage with a large potential to impact the atmospheric carbon dioxide (CO2) burden. Atmospheric CO2 observations integrate all processes affecting C exchange between the surface and the atmosphere. Therefore they provide a benchmark for carbon cycle models. We evaluated two distinct soil carbon models (CBALANCE and YASSO) that were implemented to a global land surface model (JSBACH) against atmospheric CO2 observations. We transported the biospheric carbon fluxes obtained by JSBACH using the atmospheric transport model TM5 to obtain atmospheric CO2. We then compared these results with surface observations from Global Atmosphere Watch (GAW) stations as well as with column XCO2 retrievals from the GOSAT satellite. The seasonal cycles of atmospheric CO2 estimated by the two different soil models differed. The estimates from the CBALANCE soil model were more in line with the surface observations at low latitudes (0 N–45 N) with only 1 % bias in the seasonal cycle amplitude (SCA), whereas YASSO was underestimating the SCA in this region by 32 %. YASSO gave more realistic seasonal cycle amplitudes of CO2 at northern boreal sites (north of 45 N) with underestimation of 15 % compared to 30 % overestimation by CBALANCE. Generally, the estimates from CBALANCE were more successful in capturing the seasonal patterns and seasonal cycle amplitudes of atmospheric CO2 even though it overestimated soil carbon stocks by 225 % (compared to underestimation of 36 % by YASSO) and its predictions of the global distribution of soil carbon stocks was unrealistic. The reasons for these differences in the results are related to the different environmental drivers and their functional dependencies of these two soil carbon models. In the tropical region the YASSO model showed earlier increase in season of the heterotophic respiration since it is driven by precipitation instead of soil moisture as CBALANCE. In the temperate and boreal region the role of temperature is more dominant. There the heterotophic respiration from the YASSO model had larger annual variability, driven by air temperature, compared to the CBALANCE which is driven by soil temperature. The results underline the importance of using sub-yearly data in the development of soil carbon models when they are used in shorter than annual time scales.

We estimated the CH4 budget in Finland for 2004–2014 using the CTE-CH4 data assimilation system with an extended atmospheric CH4 observation network of seven sites from Finland to surrounding regions (Hyytiälä, Kjølnes, Kumpula, Pallas, Puijo, Sodankylä, and Utö). The estimated average annual total emission for Finland is 0.6 ± 0.5 Tg CH4 yr−1. Sensitivity experiments show that the posterior biospheric emission estimates for Finland are between 0.3 and 0.9 Tg CH4 yr−1, which lies between the LPX-Bern-DYPTOP (0.2 Tg CH4 yr−1) and LPJG-WHyMe (2.2 Tg CH4 yr−1) process-based model estimates. For anthropogenic emissions, we found that the EDGAR v4.2 FT2010 inventory (0.4 Tg CH4 yr−1) is likely to overestimate emissions in southernmost Finland, but the extent of overestimation and possible relocation of emissions are difficult to derive from the current observation network. The posterior emission estimates were especially reliant on prior information in central Finland. However, based on analysis of posterior atmospheric CH4, we found that the anthropogenic emission distribution based on a national inventory is more reliable than the one based on EDGAR v4.2 FT2010. The contribution of total emissions in Finland to global total emissions is only about 0.13%, and the derived total emissions in Finland showed no trend during 2004–2014. The model using optimized emissions was able to reproduce observed atmospheric CH4 at the sites in Finland and surrounding regions fairly well (correlation > 0.75, bias < ± ppb), supporting adequacy of the observations to be used in atmospheric inversion studies. In addition to global budget estimates, we found that CTE-CH4 is also applicable for regional budget estimates, where small scale (1x1 in this case) optimization is possible with a dense observation network.

CarbonTracker Europe-CH4 (CTE-CH4) inverse model versions 1.0 and 1.1 are presented. The model optimizes global surface methane emissions from biosphere and anthropogenic sources using an ensemble Kalman filter (EnKF) based optimization method, using the TM5 chemistry transport model as an observation operator, and assimilating global in-situ atmospheric methane mole fraction observations. In this study, we examine sensitivity of our CH4 emission estimates on the ensemble size, covariance matrix, prior estimates, observations to be assimilated, assimilation window length, convection scheme in TM5, and model structure in the emission estimates by performing CTE-CH4 with several set-ups. The analyses show that the model is sensitive to most of the parameters and inputs that were examined. Firstly, using a large enough ensemble size stabilises the results. Secondly, using an informative covariance matrix reduces uncertainty estimates. Thirdly, agreement with discrete observations became better when assimilating continuous observations. Finally, the posterior emissions were found sensitive to the choice of prior estimates, convection scheme and model structure, particularly to their spatial distribution. The distribution of posterior mole fractions derived from posterior emissions is consistent with the observations to the extent prescribed in the various covariance estimates, indicating a satisfactory performance of our system.

We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006-2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2-29.7) TgCH(4) yr(-1) (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006-2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 TgCH(4) yr(-1) (2006) to 18.8 TgCH(4) yr(-1) (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3-8.2) TgCH(4) yr(-1) from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between -40 and 20% at the aircraft profile sites in France, Hungary and Poland.

Ensemble Kalman filter (EnKF) is a useful Bayesian inverse modelling method to make inference of the states of interest from observations, especially in non-linear systems with a large number of states to be estimated. This thesis presents an application of EnKF in estimation of global and regional methane budgets, where methane fluxes are inferred from atmospheric methane concentration observations. The modelling system here requires a highly non-linear atmospheric transport model to convert the state space on to the observation space, and an optimization in both spatial and temporal dimensions is desired. Methane is an important greenhouse gas, strongly influenced by anthropogenic activities, whose atmospheric concentration increased more than twice since pre-industrial times. Although its source and sink processes have been studied extensively, the mechanisms behind the increase in the 21st century atmospheric methane concentrations are still not fully understood. In this thesis, contributions of anthropogenic and natural sources to the increase in the atmospheric methane concentrations are studied by estimating the global and regional methane fluxes from anthropogenic and biospheric sources for the 21st century using an EnKF based data assimilation system (CarbonTracker Europe-CH4 ; CTE-CH4). The model was evaluated using assimilated in situ atmospheric concentration observations and various non-assimilated observations, and the model sensitivity to several setups and inputs was examined to assess the consistency of the model estimates. The key findings of this thesis include: 1) large enough ensemble size, appropriate prior error covariance, and good observation coverage are important to obtain consistent and reliable estimates, 2) CTE-CH4 was able to identify the locations and sources of the emissions that possibly contribute significantly to the increase in the atmospheric concentrations after 2007 (the Tropical and extra Tropical anthropogenic emissions), 3) Europe was found to have an insignificant or negative influence on the increase in the atmospheric CH4 concentrations in the 21st century, 4) CTE-CH4 was able to produce flux estimates that are generally consistent with various observations, but 5) the estimated fluxes are still sensitive to the number of parameters, atmospheric transport and spatial distribution of the prior fluxes. N/A

We present a global distribution of surface methane (CH4) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1 , with an increase of 18 Tg CH4 yr−1 from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations (< 2 Tg CH4 yr−1 difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH4 yr−1 ), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16–20 Tg CH4 yr−1 ). However, this study could not conclusively separate a small trend in biospheric emissions (−5 to +6.9 Tg CH4 yr−1 ) from the much larger trend in anthropogenic emissions (15–27 Tg CH4 yr−1 ). Finally, we find that the global and North American CH4 balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH4 emissions in the United States. With further developments, especially on the treatment of the atmospheric CH4 sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget., peer-reviewed

Gobal methane emissions were estimated for 2000-2012 using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, the anthropogenic and biosphere emissions of CH4 are simultaneously constrained by global atmospheric in-situ methane mole fraction observations. We use three configurations developed in Tsuruta et al. 30 (2016) to assess the sensitivity of the CH4 flux estimates to (a) the number of unknown flux scaling factors to be optimized which in turn depends on the choice of underlying land-ecosystem map, and (b) on the parametrization of vertical mixing in the atmospheric transport model TM5. The posterior emission estimates were evaluated by comparing simulations to surface in-situ observation sites, to profile observations made by aircraft, to dry air total column-averaged mole fractions (XCH4) observations from the Total Carbon Column Observing Network (TCCON), and to XCH4 retrievals from the Greenhouse 35 gases Observing SATellite (GOSAT). Our estimated posterior mean global total emissions during 2000-2012 are 516±51 Tg CH4 yr-1 , and emission estimates during 2007-2012 are 18 Tg CH4 yr-1 greater than those from 2001-2006, mainly driven by an increase in emissions from the south America temperate region, the Asia temperate region and Asia tropics. The sensitivity of the flux estimates to the underlying ecosystem map was large for the Asia temperate region and Australia, but not significant in the northern latitude regions, i.e. the north American boreal region, the north American temperate region and Europe. Instead, the posterior estimates for the northern latitude regions show larger sensitivity to the choice of convection scheme in TM5. The Gregory et al. (2000) mixing scheme with faster interhemispheric exchange leads to higher 5 estimated CH4 emissions at northern latitudes, and lower emissions in southern latitudes, compared to the estimates using Tiedtke (1989) convection scheme. Our evaluation with non-assimilated observations showed that posterior mole fractions were better matched with the observations when Gregory et al. (2000) convection scheme was used., peer-reviewed

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which biosphere and anthropogenic emissions were optimized. Atmospheric CH4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior estimates were more than halved. Furthermore, inclusion of NOAA observations of CH, from weekly discrete air samples collected at Pallas improved agreement between posterior CH4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland.