Measurements of Pollution in the Troposphere (MOPITT) satellite and ground-based carbon monoxide (CO) measurements both suggest a widespread downward trend in CO concentrations over East Asia during the period 2005-2016. This negative trend is inconsistent with global bottom-up inventories of CO emissions, which show a small increase or stable emissions in this region. We try to reconcile the observed CO trend with emission inventories using an atmospheric inversion of the MOPITT CO data that estimates emissions from primary sources, secondary production, and chemical sinks of CO. The atmospheric inversion indicates a ∼ −2% yr −1 decrease in emissions from primary sources in East Asia from 2005-2016. The decreasing emissions are mainly caused by source reductions in China. The regional MEIC inventory for China is the only bottom up estimate consistent with the inversion-diagnosed decrease of CO emissions. According to the MEIC data, decreasing CO emissions from four main sectors (iron and steel industries, residential sources, gasoline-powered vehicles, and construction materials industries) in China explain 76% of the inversion-based trend of East Asian CO emissions. This result suggests that global inventories underestimate the recent decrease of CO emission factors in China which occurred despite increasing consumption of carbon-based fuels, and is driven by rapid technological changes with improved combustion efficiency and emission control measures.

The large peatland carbon stocks in the land use change‐affected areas of equatorial Asia are vulnerable to fire. Combining satellite observations of active fire, burned area, and atmospheric concentrations of combustion tracers with a Bayesian inversion, we estimated the amount and variability of fire carbon emissions in equatorial Asia over the period 1997–2015. Emissions in 2015 were of 0.51 ± 0.17 Pg carbon—less than half of the emissions from the previous 1997 extreme El Niño, explained by a less acute water deficit. Fire severity could be empirically hindcasted from the cumulative water deficit with a lead time of 1 to 2 months. Based on CMIP5 climate projections and an exponential empirical relationship found between fire carbon emissions and water deficit, we infer a total fire carbon loss ranging from 12 to 25 Pg by 2100 which is a significant positive feedback to climate warming.

Abstract. Cities currently covering only a very small portion ( <  3 %) of the world's land surface directly release to the atmosphere about 44 % of global energy-related CO2, but they are associated with 71–76 % of CO2 emissions from global final energy use. Although many cities have set voluntary climate plans, their CO2 emissions are not evaluated by the monitoring, reporting, and verification (MRV) procedures that play a key role for market- or policy-based mitigation actions. Here we analyze the potential of a monitoring tool that could support the development of such procedures at the city scale. It is based on an atmospheric inversion method that exploits inventory data and continuous atmospheric CO2 concentration measurements from a network of stations within and around cities to estimate city CO2 emissions. This monitoring tool is configured for the quantification of the total and sectoral CO2 emissions in the Paris metropolitan area (∼  12 million inhabitants and 11.4 TgC emitted in 2010) during the month of January 2011. Its performances are evaluated in terms of uncertainty reduction based on observing system simulation experiments (OSSEs). They are analyzed as a function of the number of sampling sites (measuring at 25 m a.g.l.) and as a function of the network design. The instruments presently used to measure CO2 concentrations at research stations are expensive (typically ∼  EUR 50 k per sensor), which has limited the few current pilot city networks to around 10 sites. Larger theoretical networks are studied here to assess the potential benefit of hypothetical operational lower-cost sensors. The setup of our inversion system is based on a number of diagnostics and assumptions from previous city-scale inversion experiences with real data. We find that, given our assumptions underlying the configuration of the OSSEs, with 10 stations only the uncertainty for the total city CO2 emission during 1 month is significantly reduced by the inversion by ∼  42 %. It can be further reduced by extending the network, e.g., from 10 to 70 stations, which is promising for MRV applications in the Paris metropolitan area. With 70 stations, the uncertainties in the inverted emissions are reduced significantly over those obtained using 10 stations: by 32 % for commercial and residential buildings, by 33 % for road transport, by 18 % for the production of energy by power plants, and by 31 % for total emissions. These results indicate that such a high number of stations would be likely required for the monitoring of sectoral emissions in Paris using this observation–model framework. They demonstrate some high potential that atmospheric inversions can contribute to the monitoring and/or the verification of city CO2 emissions (baseline) and CO2 emission reductions (commitments) and the advantage that could be brought by the current developments of lower-cost medium precision (LCMP) sensors.

Abstract. The role of soil microorganisms in regulating soil organic matter (SOM) decomposition is of primary importance in the carbon cycle, in particular in the context of global change. Modeling soil microbial community dynamics to simulate its impact on soil gaseous carbon (C) emissions and nitrogen (N) mineralization at large spatial scales is a recent research field with the potential to improve predictions of SOM responses to global climate change. In this study we present a SOM model called ORCHIMIC, which utilizes input data that are consistent with those of global vegetation models. ORCHIMIC simulates the decomposition of SOM by explicitly accounting for enzyme production and distinguishing three different microbial functional groups: fresh organic matter (FOM) specialists, SOM specialists, and generalists, while also implicitly accounting for microbes that do not produce extracellular enzymes, i.e., cheaters. ORCHIMIC and two other organic matter decomposition models, CENTURY (based on first-order kinetics and representative of the structure of most current global soil carbon models) and PRIM (with FOM accelerating the decomposition rate of SOM), were calibrated to reproduce the observed respiration fluxes of FOM and SOM from the incubation experiments of Blagodatskaya et al. (2014). Among the three models, ORCHIMIC was the only one that effectively captured both the temporal dynamics of the respiratory fluxes and the magnitude of the priming effect observed during the incubation experiment. ORCHIMIC also effectively reproduced the temporal dynamics of microbial biomass. We then applied different idealized changes to the model input data, i.e., a 5 K stepwise increase of temperature and/or a doubling of plant litter inputs. Under 5 K warming conditions, ORCHIMIC predicted a 0.002 K−1 decrease in the C use efficiency (defined as the ratio of C allocated to microbial growth to the sum of C allocated to growth and respiration) and a 3 % loss of SOC. Under the double litter input scenario, ORCHIMIC predicted a doubling of microbial biomass, while SOC stock increased by less than 1 % due to the priming effect. This limited increase in SOC stock contrasted with the proportional increase in SOC stock as modeled by the conventional SOC decomposition model (CENTURY), which can not reproduce the priming effect. If temperature increased by 5 K and litter input was doubled, ORCHIMIC predicted almost the same loss of SOC as when only temperature was increased. These tests suggest that the responses of SOC stock to warming and increasing input may differ considerably from those simulated by conventional SOC decomposition models when microbial dynamics are included. The next step is to incorporate the ORCHIMIC model into a global vegetation model to perform simulations for representative sites and future scenarios.

Abstract. Erosion is an Earth system process that transports carbon laterally across the land surface and is currently accelerated by anthropogenic activities. Anthropogenic land cover change has accelerated soil erosion rates by rainfall and runoff substantially, mobilizing vast quantities of soil organic carbon (SOC) globally. At timescales of decennia to millennia this mobilized SOC can significantly alter previously estimated carbon emissions from land use change (LUC). However, a full understanding of the impact of erosion on land-atmosphere carbon exchange is still missing. The aim of this study is to better constrain the terrestrial carbon fluxes by developing methods compatible with land surface models (LSMs) in order to explicitly represent the links between soil erosion by rainfall and runoff and carbon dynamics. For this we use an emulator that represents the carbon cycle of a LSM, in combination with the Revised Universal Soil Loss Equation (RUSLE) model. We applied this modeling framework at the global scale to evaluate the effects of potential soil erosion (soil removal only) in the presence of other perturbations of the carbon cycle: elevated atmospheric CO 2 , climate variability, and LUC. We find that over the period AD 1850-2005 acceleration of soil erosion leads to a total potential SOC removal flux of 74 ± 18 Pg C, of which 79 %-85 % occurs on agricultural land and grassland. Using our best estimates for soil erosion we find that including soil erosion in the SOC-dynamics scheme results in an increase of 62 % of the cumulative loss of SOC over 1850-2005 due to the combined effects of climate variability, increasing atmospheric CO 2 and LUC. This additional erosional loss decreases the cumulative global carbon sink on land by 2 Pg of carbon for this specific period, with the largest effects found for the tropics, where deforestation and agricultural expansion increased soil erosion rates significantly. We conclude that the potential effect of soil erosion on the global SOC stock is comparable to the effects of climate or LUC. It is thus necessary to include soil erosion in assessments of LUC and evaluations of the terrestrial carbon cycle.

Satellite estimates of burned area, associated carbon monoxide (CO) emission estimates, and CO column retrievals do not agree on the peak fire month in Africa, evident in both Northern and Southern Africa though distinct in the burning seasonality. Here we analyze this long‐standing problem using (1) a top‐down Bayesian inversion of Measurements Of Pollution In The Troposphere CO columns during 2005–2016 into surface CO emissions and (2) the bottom‐up Global Fire Emissions Database 4.1 s. We show that Global Fire Emissions Database 4.1 s underestimates CO emissions by 12–62% in the late fire season and hypothesize that this is partly because it assumes seasonally static emission factors. However, the degree to which emission factors would have to vary through the season to bring top‐down and bottom‐up in agreement cannot be confirmed by past field‐based measurements. Improved observational constraint on the seasonality of burned area, fuel combustion, and emission factors would further reduce the discrepancy between bottom‐up and top‐down emission estimates.

The pressure wave of the abdominal aorta and the flow wave of the renal artery were recorded simultaneously from a rat. The impedance of a kidney system that is derived by dividing the pressure of the corresponding frequency by that of the flow was studied in six rats. The data show that the system has two resonant frequencies, at the second and third harmonics. At the second harmonic, the pressure wave and fluid flow in a round trip through the branch of the kidney. Whereas it is difficult for the third harmonic flow to enter the kidney, it flows directly through the aorta. To obtain further proof, we compared the frequency components of the two flows measured simultaneously on the abdominal aorta and the renal artery and found the same result. The kidney, renal artery, and aorta combined show a coupled oscillation that is analogous to that of resonance circuits. The kidney vascular system exhibits a resonant frequency at the second harmonic of the heartbeat.

Abstract. The high-latitude regions of the northern hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance – those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest – are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently-developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input data sets, are extensively evaluated against: (i) temperature gradients between the atmosphere and deep soils; (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.