Inverse modeling techniques used to quantify surface carbon fluxes commonly assume that the uncertainty of fossil fuel CO(2) (FFCO(2)) emissions is negligible and that intra-annual variations can be neglected. To investigate these assumptions, we analyzed the differences between four fossil fuel emission inventories with spatial and temporal differences over Europe and their impact on the model simulated CO(2) concentration. Large temporal flux variations characterize the hourly fields (similar to 40% and similar to 80% for the seasonal and diurnal cycles, peak-to-peak) and annual country totals differ by 10% on average and up to 40% for some countries (i.e., the Netherlands). These emissions have been prescribed to seven different transport models, resulting in 28 different FFCO(2) concentrations fields. The modeled FFCO(2) concentration time series at surface sites using time-varying emissions show larger seasonal cycles (+2 ppm at the Hungarian tall tower (HUN)) and smaller diurnal cycles in summer (-1 ppm at HUN) than when using constant emissions. The concentration range spanned by all simulations varies between stations, and is generally larger in winter (up to similar to 10 ppm peak-to-peak at HUN) than in summer (similar to 5 ppm). The contribution of transport model differences to the simulated concentration std-dev is 2-3 times larger than the contribution of emission differences only, at typical European sites used in global inversions. These contributions to the hourly (monthly) std-dev's amount to similar to 1.2 (0.8) ppm and similar to 0.4 (0.3) ppm for transport and emissions, respectively. First comparisons of the modeled concentrations with (14)C-based fossil fuel CO(2) observations show that the large transport differences still hamper a quantitative evaluation/validation of the emission inventories. Changes in the estimated monthly biosphere flux (Fbio) over Europe, using two inverse modeling approaches, are relatively small (less that 5 %) while changes in annual Fbio (up to similar to 0.15% GtC yr(-1)) are only slightly smaller than the differences in annual emission totals and around 30% of the mean European ecosystem carbon sink. These results point to an urgent need to improve not only the transport models but also the assumed spatial and temporal distribution of fossil fuel emission inventories

On regional to global scales, few constraints exist on gross primary productivity (GPP) and ecosystem respiration (R e ) fluxes. Yet constraints on these fluxes are critical for evaluating and improving terrestrial biosphere models. In this study, we evaluate the seasonal cycle of GPP, R e , and net ecosystem exchange (NEE) produced by four terrestrial biosphere models and FLUXCOM, a data-driven model, over northern midlatitude ecosystems. We evaluate the seasonal cycle of GPP and NEE using solar-induced fluorescence retrieved from the Global Ozone Monitoring Experiment-2 and column-averaged dry-air mole fractions of CO 2 (X CO 2 ) from the Total Carbon Column Observing Network, respectively. We then infer R e by combining constraints on GPP with constraints on NEE from two flux inversions. An ensemble of optimized R e seasonal cycles is generated using five GPP estimates and two NEE estimates. The optimized R e curves generally show high consistency with each other, with the largest differences due to the magnitude of GPP. We find optimized R e exhibits a systematically broader summer maximum than modeled R e , with values lower during June-July and higher during the fall than R e . Further analysis suggests that the differences could be due to seasonal variations in the carbon use efficiency (possibly due to an ecosystem-scale Kok effect) and to seasonal variations in the leaf litter and fine root carbon pool. The results suggest that the inclusion of variable carbon use efficiency for autotrophic respiration and carbon pool dependence for heterotrophic respiration is important for accurately simulating R e .

Abstract. Inverse modeling techniques used to quantify surface carbon fluxes commonly assume that the uncertainty of fossil fuel CO2 (FFCO2) emissions is negligible and that intra-annual variations can be neglected. To investigate these assumptions, we analyzed the differences between four fossil fuel emission maps with spatial and temporal differences over Europe and their impact on the model simulated CO2 concentration. Large temporal flux variations characterize the hourly fields (~40% and ~80% for the seasonal and diurnal cycles, peak-to-peak) and annual country totals differ by 10% on average and up to 40% for some countries (i.e., The Netherlands). These emissions have been prescribed to seven different transport models, resulting in 28 different FFCO2 concentrations fields. The modeled FFCO2 concentration time series at surface sites using time-varying emissions show larger seasonal cycles (+2 ppm at the Hungarian tall tower (HUN)) and smaller diurnal cycles in summer (−1 ppm at HUN) than when using constant emissions. The concentration range spanned by all simulations varies between stations, and is generally larger in winter (up to ~10 ppm peak-to-peak at HUN) than in summer (~5 ppm). The contribution of transport model differences to the simulated concentration std-dev is 2–3 times larger than the contribution of emission differences only, at typical European sites used in global inversions. These contributions to the hourly (monthly) std-dev's amount to ~1.2 (0.8) ppm and ~0.4 (0.3) ppm for transport and emissions, respectively. First comparisons of the modeled concentrations with 14C-based fossil fuel CO2 observations show that the large transport differences still hamper a quantitative evaluation/validation of the emission inventories. Changes in the estimated monthly biosphere flux (Fbio) over Europe, using two inverse modeling approaches, are relatively small (less that 5%) while changes in annual Fbio (up to ~0.15 Gt C/yr) are only slightly smaller than the differences in annual emission totals and around 30% of the mean European ecosystem carbon sink. These results point to an urgent need to improve not only the transport models but also the assumed spatial and temporal distribution of fossil fuel emission maps.

Abstract. Climate sensitivity in Earth system models (ESMs) is an emergent property that is affected by structural (missing or inaccurate model physics) and parametric (variations in model parameters) uncertainty. This work provides the first quantitative assessment of the role of compensation between uncertainties in aerosol forcing and atmospheric parameters, and their impact on the climate sensitivity of the Community Atmosphere Model, Version 4 (CAM4). Running the model with prescribed ocean and ice conditions, we perturb four parameters related to sulfate and black carbon aerosol radiative forcing and distribution, as well as five atmospheric parameters related to clouds, convection, and radiative flux. In this experimental setup where aerosols do not affect the properties of clouds, the atmospheric parameters explain the majority of variance in climate sensitivity, with two parameters being the most important: one controlling low cloud amount, and one controlling the timescale for deep convection. Although the aerosol parameters strongly affect aerosol optical depth, their impacts on climate sensitivity are substantially weaker than the impacts of the atmospheric parameters, but this result may depend on whether aerosol–cloud interactions are simulated. Based on comparisons to inter-model spread of other ESMs, we conclude that structural uncertainties in this configuration of CAM4 likely contribute 3 times more to uncertainty in climate sensitivity than parametric uncertainties. We provide several parameter sets that could provide plausible (measured by a skill score) configurations of CAM4, but with different sulfate aerosol radiative forcing, black carbon radiative forcing, and climate sensitivity.