Towards a Tool for Forward and Reverse Mode Source-to-Source Transformation in C/C++

Summary. The paper presents a feasibility study for TAC++, the C/C++equivalen

Simultaneous assimilation of remotely sensed soil moisture and FAPAR for improving terrestrial carbon fluxes at multiple sites using CCDAS

The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO2 fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre-Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO2. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands

Assimilation of atmospheric CO2observations from space can support national CO2emission inventories

The Paris Agreement establishes a transparency framework for anthropogenic carbon dioxide (CO2) emissions. It's core component are inventory-based national greenhouse gas emission reports, which are complemented by independent estimates derived from atmospheric CO2 measurements combined with inverse modelling. It is, however, not known whether such a Monitoring and Verification Support (MVS) capacity is capable of constraining estimates of fossil-fuel emissions to an extent that is sufficient to provide valuable additional information. The CO2 Monitoring Mission (CO2M), planned as a constellation of satellites measuring column-integrated atmospheric CO2 concentration (XCO2), is expected to become a key component of such an MVS capacity. Here we provide a novel assessment of the potential of a comprehensive data assimilation system using simulated XCO2 and other observations to constrain fossil fuel CO2 emission estimates for an exemplary 1-week period in 2008. We find that CO2M enables useful weekly estimates of country-scale fossil fuel emissions independent of national inventories. When extrapolated from the weekly to the annual scale, uncertainties in emissions are comparable to uncertainties in inventories, so that estimates from inventories and from the MVS capacity can be used for mutual verification. We further demonstrate an alternative, synergistic mode of operation, with the purpose of delivering a best fossil fuel emission estimate. In this mode, the assimilation system uses not only XCO2 and the other data streams of the previous (verification) mode, but also the inventory information. Finally, we identify further steps towards an operational MVS capacity

Assessing the Impact of Atmospheric CO2 and NO2 Measurements From Space on Estimating City-Scale Fossil Fuel CO2 Emissions in a Data Assimilation System

The European Copernicus programme plans to install a constellation of multiple polar orbiting satellites (Copernicus Anthropogenic CO2 Monitoring Mission, CO2M mission) for observing atmospheric CO2 content with the aim to estimate fossil fuel CO2 emissions. We explore the impact of potential CO2M observations of column-averaged CO2 (XCO2), nitrogen dioxide (NO2), and aerosols in a 200 × 200 km2 domain around Berlin. For the quantification of anticipated XCO2 random and systematic errors we developed and applied new error parameterisation formulae based on artificial neural networks. For the interpretation of these data, we further established a CCFFDAS modelling chain from parameters of emission models to XCO2 and NO2 observations to simulate the 24 h periods preceeding simulated CO2M overpasses over the study area. For one overpass in winter and one in summer, we present a number of assessments of observation impact in terms of the posterior uncertainty in fossil fuel emissions on scales ranging from 2 to 200 km. This means the assessments include temporal and spatial scales typically not covered by inventories. The assessments differentiate the fossil fuel CO2 emissions into two sectors, an energy generation sector (power plants) and the complement, which we call “other sector.” We find that combined measurements of XCO2 and aerosols provide a powerful constraint on emissions from larger power plants; the uncertainty in fossil fuel emissions from the largest three power plants in the domain was reduced by 60%–90% after assimilating the observations. Likewise, these measurements achieve an uncertainty reduction for the other sector that increases when aggregated to larger spatial scales. When aggregated over Berlin the uncertainty reduction for the other sector varies between 28% and 48%. Our assessments show a considerable contribution of aerosol observations onboard CO2M to the constraint of the XCO2 measurements on emissions from all power plants and for the other sector on all spatial scales. NO2 measurements onboard CO2M provide a powerful additional constraint on the emissions from power plants and from the other sector. We further apply a Jacobian representation of the CCFFDAS modelling chain to decompose a simulated CO2 column in terms of spatial emission impact. This analysis reveals the complex structure of the footprint of an observed CO2 column, which indicates the limits of simple mass balances approaches for interpretation of such observations

Assessing the Impact of Atmospheric CO2 and NO2 Measurements From Space on Estimating City-Scale Fossil Fuel CO2 Emissions in a Data Assimilation System

The European Copernicus programme plans to install a constellation of multiple polar orbiting satellites (Copernicus Anthropogenic CO2 Monitoring Mission, CO2M mission) for observing atmospheric CO2 content with the aim to estimate fossil fuel CO2 emissions. We explore the impact of potential CO2M observations of column-averaged CO2 (XCO2), nitrogen dioxide (NO2), and aerosols in a 200 × 200 km2 domain around Berlin. For the quantification of anticipated XCO2 random and systematic errors we developed and applied new error parameterisation formulae based on artificial neural networks. For the interpretation of these data, we further established a CCFFDAS modelling chain from parameters of emission models to XCO2 and NO2 observations to simulate the 24 h periods preceeding simulated CO2M overpasses over the study area. For one overpass in winter and one in summer, we present a number of assessments of observation impact in terms of the posterior uncertainty in fossil fuel emissions on scales ranging from 2 to 200 km. This means the assessments include temporal and spatial scales typically not covered by inventories. The assessments differentiate the fossil fuel CO2 emissions into two sectors, an energy generation sector (power plants) and the complement, which we call “other sector.” We find that combined measurements of XCO2 and aerosols provide a powerful constraint on emissions from larger power plants; the uncertainty in fossil fuel emissions from the largest three power plants in the domain was reduced by 60%–90% after assimilating the observations. Likewise, these measurements achieve an uncertainty reduction for the other sector that increases when aggregated to larger spatial scales. When aggregated over Berlin the uncertainty reduction for the other sector varies between 28% and 48%. Our assessments show a considerable contribution of aerosol observations onboard CO2M to the constraint of the XCO2 measurements on emissions from all power plants and for the other sector on all spatial scales. NO2 measurements onboard CO2M provide a powerful additional constraint on the emissions from power plants and from the other sector. We further apply a Jacobian representation of the CCFFDAS modelling chain to decompose a simulated CO2 column in terms of spatial emission impact. This analysis reveals the complex structure of the footprint of an observed CO2 column, which indicates the limits of simple mass balances approaches for interpretation of such observations