Emissions into the atmosphere from human activities show marked temporal variations, from inter-annual to hourly levels. the consolidated practice of calculating yearly emissions follows the same temporal allocation of the underlying annual statistics. However, yearly emissions might not reflect heavy pollution episodes, seasonal trends, or any time-dependant atmospheric process. This study develops high-time resolution profiles for air pollutants and greenhouse gases co-emitted by anthropogenic sources in support of atmospheric modelling, Earth observation communities and decision makers. the key novelties of the Emissions Database for Global atmospheric Research (EDGAR) temporal profiles are the development of (i) country/region-and sector-specific yearly profiles for all sources, (ii) time dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, (iii) country-specific weekly and daily profiles to represent hourly emissions, (iv) a flexible system to compute hourly emissions including input from different users. This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises.

This is a repository copy of Acceleration of global N2O emissions seen from two decades of atmospheric inversion.

[1] We describe a system for constraining the spatial distribution of fossil fuel emissions of CO 2 . The system is based on a modified Kaya identity which expresses emissions as a product of areal population density, per capita economic activity, energy intensity of the economy, and carbon intensity of energy. We apply the methodology of data assimilation to constrain such a model with various observations, notably, the statistics of national emissions and data on the distribution of nightlights and population. We hence produce a global, annual emission field at 0.25°resolution. Our distribution of emissions is smoother than that of the population downscaling traditionally used to describe emissions. Comparison with the Vulcan inventory suggests that the assimilated product performs better than downscaling for distributions of either population or nightlights alone for describing the spatial structure of emissions over the United States. We describe the complex structure of uncertainty that arises from combining pointwise and area-integrated constraints. Uncertainties can be as high as 50% at the pixel level and are not spatially independent. We describe the use of 14 CO 2 measurements to further constrain national emissions. Their value is greatest over large countries with heterogeneous emissions. Generated fields may be found online (http://ffdas.org/).Citation: Rayner, P. J., M. R. Raupach, M. Paget, P. Peylin, and E. Koffi (2010), A new global gridded data set of CO 2 emissions from fossil fuel combustion: Methodology and evaluation,

Abstract. Large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr−1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.

[1] We present the concept of the Carbon Cycle Data Assimilation System and describe its evolution over the last two decades from an assimilation system around a simple diagnostic model of the terrestrial biosphere to a system for the calibration and initialization of the land component of a comprehensive Earth system model. We critically review the capability of this modeling framework to integrate multiple data streams, to assess their mutual consistency and with the model, to reduce uncertainties in the simulation of the terrestrial carbon cycle, to provide, in a traceable manner, reanalysis products with documented uncertainty, and to assist the design of the observational network. We highlight some of the challenges we met and experience we gained, give recommendations for operating the system, and suggest directions for future development.

Abstract. Simulations of carbon fluxes with terrestrial biosphere models still exhibit significant uncertainties, in part due to the uncertainty in model parameter values. With the advent of satellite measurements of solar induced chlorophyll fluorescence (SIF), there exists a novel pathway for constraining simulated carbon fluxes and parameter values. We investigate the utility of SIF in constraining gross primary productivity (GPP). As a first test we assess whether SIF simulations are sensitive to important parameters in a biosphere model. SIF measurements at the wavelength of 755 nm are simulated by the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. Idealized sensitivity tests of the SCOPE model stand-alone indicate strong sensitivity of GPP to the carboxylation capacity (Vcmax) and of SIF to the chlorophyll AB content (Cab) and incoming short wave radiation. Low sensitivity is found for SIF to Vcmax, however the relationship is subtle, with increased sensitivity under high radiation conditions and lower Vcmax ranges. CCDAS simulates well the patterns of satellite-measured SIF suggesting the combined model is capable of ingesting the data. CCDAS supports the idealized sensitivity tests of SCOPE, with SIF exhibiting sensitivity to Cab and incoming radiation, both of which are treated as perfectly known in previous CCDAS versions. These results demonstrate the need for careful consideration of Cab and incoming radiation when interpreting SIF and the limitations of utilizing SIF to constrain Vcmax in the present set-up in the CCDAS system.

[1] This paper combines an atmospheric transport model and a terrestrial ecosystem model to estimate gross primary productivity (GPP) and net ecosystem productivity (NEP) of the land biosphere. Using atmospheric CO 2 observations in a Carbon Cycle Data Assimilation System (CCDAS) we estimate a terrestrial global GPP of 146 AE 19 GtC/yr. However, the current observing network cannot distinguish this best estimate from a different assimilation experiment yielding a terrestrial global GPP of 117 GtC/yr. Spatial estimates of GPP agree with data-driven estimates in the extratropics but are overestimated in the poorly observed tropics. The uncertainty analysis of previous studies was extended by using two atmospheric transport models and different CO 2 observing networks. We find that estimates of GPP and NEP are less sensitive to these choices than the form of the prior probability for model parameters. NEP is also found to be significantly sensitive to the transport model and this sensitivity is not greatly reduced compared to direct atmospheric transport inversions, which optimize NEP directly.

Abstract. This paper presents the assimilation of solar-induced chlorophyll fluorescence (SIF) into a terrestrial biosphere model to estimate the gross uptake of carbon through photosynthesis (GPP). We use the BETHY-SCOPE model to simulate both GPP and SIF using a process-based formulation, going beyond a simple linear scaling between the two. We then use satellite SIF data from the Orbiting Carbon Observatory-2 (OCO-2) for 2015 in the data assimilation system to constrain model biophysical parameters and GPP. The assimilation results in considerable improvement in the fit between model and observed SIF, despite a limited capability to fit regions with large seasonal variability in SIF. The SIF assimilation increases global GPP by 31 % to 167±5 Pg C yr−1 and shows an improvement in the global distribution of productivity relative to independent estimates, but a large difference in magnitude. This change in global GPP is driven by an overall increase in photosynthetic light-use efficiency across almost all biomes and more minor, regionally distinct changes in APAR. This process-based data assimilation opens up new pathways to the effective utilization of satellite SIF data to improve our understanding of the global carbon cycle.