The global land and ocean carbon sinks have increased proportionally with increasing carbon dioxide emissions during the past decades. It is thought that Northern Hemisphere lands make a dominant ...contribution to the global land carbon sink; however, the long-term trend of the northern land sink remains uncertain. Here, using measurements of the interhemispheric gradient of atmospheric carbon dioxide from 1958 to 2016, we show that the northern land sink remained stable between the 1960s and the late 1980s, then increased by 0.5 ± 0.4 petagrams of carbon per year during the 1990s and by 0.6 ± 0.5 petagrams of carbon per year during the 2000s. The increase of the northern land sink in the 1990s accounts for 65% of the increase in the global land carbon flux during that period. The subsequent increase in the 2000s is larger than the increase in the global land carbon flux, suggesting a coincident decrease of carbon uptake in the Southern Hemisphere. Comparison of our findings with the simulations of an ensemble of terrestrial carbon models over the same period suggests that the decadal change in the northern land sink between the 1960s and the 1990s can be explained by a combination of increasing concentrations of atmospheric carbon dioxide, climate variability and changes in land cover. However, the increase during the 2000s is underestimated by all models, which suggests the need for improved consideration of changes in drivers such as nitrogen deposition, diffuse light and land-use change. Overall, our findings underscore the importance of Northern Hemispheric land as a carbon sink.
Atmospheric concentration measurements are used to adjust the daily to monthly budget of fossil fuel CO2 emissions of the Paris urban area from the prior estimates established by the Airparif local ...air quality agency. Five atmospheric monitoring sites are available, including one at the top of the Eiffel Tower. The atmospheric inversion is based on a Bayesian approach, and relies on an atmospheric transport model with a spatial resolution of 2 km with boundary conditions from a global coarse grid transport model. The inversion adjusts prior knowledge about the anthropogenic and biogenic CO2 fluxes from the Airparif inventory and an ecosystem model, respectively, with corrections at a temporal resolution of 6 h, while keeping the spatial distribution from the emission inventory. These corrections are based on assumptions regarding the temporal autocorrelation of prior emissions uncertainties within the daily cycle, and from day to day. The comparison of the measurements against the atmospheric transport simulation driven by the a priori CO2 surface fluxes shows significant differences upwind of the Paris urban area, which suggests a large and uncertain contribution from distant sources and sinks to the CO2 concentration variability. This contribution advocates that the inversion should aim at minimising model-data misfits in upwind-downwind gradients rather than misfits in mole fractions at individual sites. Another conclusion of the direct model-measurement comparison is that the CO2 variability at the top of the Eiffel Tower is large and poorly represented by the model for most wind speeds and directions. The model's inability to reproduce the CO2 variability at the heart of the city makes such measurements ill-suited for the inversion. This and the need to constrain the budgets for the whole city suggests the assimilation of upwind-downwind mole fraction gradients between sites at the edge of the urban area only. The inversion significantly improves the agreement between measured and modelled concentration gradients. Realistic emissions are retrieved for two 30-day periods and suggest a significant overestimate by the AirParif inventory. Similar inversions over longer periods are necessary for a proper evaluation of the optimised CO2 emissions against independent data.
Negative trends of carbon monoxide (CO) concentrations are observed in the recent decade by both surface measurements and satellite retrievals over many regions of the globe, but they are not well ...explained by current emission inventories. Here, we analyse the observed CO concentration decline with an atmospheric inversion that simultaneously optimizes the two main CO sources (surface emissions and atmospheric hydrocarbon oxidations) and the main CO sink (atmospheric hydroxyl radical OH oxidation). Satellite CO column retrievals from Measurements of Pollution in the Troposphere (MOPITT), version 6, and surface observations of methane and methyl chloroform mole fractions are assimilated jointly for the period covering 2002–2011. Compared to the model simulation prescribed with prior emission inventories, trends in the optimized CO concentrations show better agreement with that of independent surface in situ measurements. At the global scale, the atmospheric inversion primarily interprets the CO concentration decline as a decrease in the CO emissions (−2.3 % yr−1), more than twice the negative trend estimated by the prior emission inventories (−1.0 % yr−1). The spatial distribution of the inferred decrease in CO emissions indicates contributions from western Europe (−4.0 % yr−1), the United States (−4.6 % yr−1) and East Asia (−1.2 % yr−1), where anthropogenic fuel combustion generally dominates the overall CO emissions, and also from Australia (−5.3 % yr−1), the Indo-China Peninsula (−5.6 % yr−1), Indonesia (−6.7 % y−1), and South America (−3 % yr−1), where CO emissions are mostly due to biomass burning. In contradiction with the bottom-up inventories that report an increase of 2 % yr−1 over China during the study period, a significant emission decrease of 1.1 % yr−1 is inferred by the inversion. A large decrease in CO emission factors due to technology improvements would outweigh the increase in carbon fuel combustions and may explain this decrease. Independent satellite formaldehyde (CH2O) column retrievals confirm the absence of large-scale trends in the atmospheric source of CO. However, it should be noted that the CH2O retrievals are not assimilated and OH concentrations are optimized at a very large scale in this study.
The 2018 drought was one of the worst European droughts of the twenty-first century in terms of its severity, extent and duration. The effects of the drought could be seen in a reduction in harvest ...yields in parts of Europe, as well as an unprecedented browning of vegetation in summer. Here, we quantify the effect of the drought on net ecosystem exchange (NEE) using five independent regional atmospheric inversion frameworks. Using a network of atmospheric CO
mole fraction observations, we estimate NEE with at least monthly and 0.5° × 0.5° resolution for 2009-2018. We find that the annual NEE in 2018 was likely more positive (less CO
uptake) in the temperate region of Europe by 0.09 ± 0.06 Pg C yr
(mean ± s.d.) compared to the mean of the last 10 years of -0.08 ± 0.17 Pg C yr
, making the region close to carbon neutral in 2018. Similarly, we find a positive annual NEE anomaly for the northern region of Europe of 0.02 ± 0.02 Pg C yr
compared the 10-year mean of -0.04 ± 0.05 Pg C yr
. In both regions, this was largely owing to a reduction in the summer CO
uptake. The positive NEE anomalies coincided spatially and temporally with negative anomalies in soil water. These anomalies were exceptional for the 10-year period of our study. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.
A method to constrain carbon dioxide (CO2 ) emissions from open biomass burning by using satellite observations of co-emitted species and a chemistry-transport model (CTM) is proposed and applied to ...the case of wildfires in Siberia. CO2 emissions are assessed by means of an emission model assuming a direct relationship between the biomass burning rate (BBR) and the fire radiative power (FRP) derived from MODIS measurements. The key features of the method are (1) estimating the FRP-to-BBR conversion factors (α) for different vegetative land cover types by assimilating the satellite observations of co-emitted species into the CTM, (2) optimal combination of the estimates of α derived independently from satellite observations of different species (CO and aerosol in this study), and (3) estimation of the diurnal cycle of the fire emissions directly from the FRP measurements. Values of α for forest and grassland fires in Siberia and their uncertainties are estimated using the Infrared Atmospheric Sounding Interferometer (IASI) carbon monoxide (CO) retrievals and MODIS aerosol optical depth (AOD) measurements combined with outputs from the CHIMERE mesoscale chemistry-transport model. The constrained CO emissions are validated through comparison of the respective simulations with independent data of ground-based CO measurements at the ZOTTO site. Using our optimal regional-scale estimates of the conversion factors (which are found to be in agreement with earlier published estimates obtained from local measurements of experimental fires), the total CO2 emissions from wildfires in Siberia in 2012 are estimated to be in the range from 280 to 550 Tg C, with the optimal (maximum likelihood) value of 392 Tg C. Sensitivity test cases featuring different assumptions regarding the injection height and diurnal variations of emissions indicate that the derived estimates of the total CO2 emissions in Siberia are robust with respect to the modeling options (the different estimates vary within less than 15% of their magnitude). The CO2 emission estimates obtained for several years are compared with independent estimates provided by the GFED3.1 and GFASv1.0 global emission inventories. It is found that our "top-down" estimates for the total annual biomass burning CO2 emissions in the period from 2007 to 2011 in Siberia are by factors of 2.5 and 1.8 larger than the respective bottom-up estimates; these discrepancies cannot be fully explained by uncertainties in our estimates. There are also considerable differences in the spatial distribution of the different emission estimates; some of those differences have a systematic character and require further analysis.
We present a performance assessment of the European Integrated Carbon Observing System (ICOS) atmospheric network for constraining European biogenic CO2 fluxes (hereafter net ecosystem exchange, ...NEE). The performance of the network is assessed in terms of uncertainty in the fluxes, using a state-of-the-art mesoscale variational atmospheric inversion system assimilating hourly averages of atmospheric data to solve for NEE at 6 h and 0.5∘ resolution. The performance of the ICOS atmospheric network is also assessed in terms of uncertainty reduction compared to typical uncertainties in the flux estimates from ecosystem models, which are used as prior information by the inversion. The uncertainty in inverted fluxes is computed for two typical periods representative of northern summer and winter conditions in July and in December 2007, respectively. These computations are based on a observing system simulation experiment (OSSE) framework. We analyzed the uncertainty in a 2-week-mean NEE as a function of the spatial scale with a focus on the model native grid scale (0.5∘), the country scale and the European scale (including western Russia and Turkey). Several network configurations, going from 23 to 66 sites, and different configurations of the prior uncertainties and atmospheric model transport errors are tested in order to assess and compare the improvements that can be expected in the future from the extension of the network, from improved prior information or transport models. Assimilating data from 23 sites (a network comparable to present-day capability) with errors estimated from the present prior information and transport models, the uncertainty reduction on a 2-week-mean NEE should range between 20 and 50 % for 0.5∘ resolution grid cells in the best sampled area encompassing eastern France and western Germany. At the European scale, the prior uncertainty in a 2-week-mean NEE is reduced by 50 % (66 %), down to ∼ 43 Tg C month-1 (26 Tg C month-1) in July (December). Using a larger network of 66 stations, the prior uncertainty of NEE is reduced by the inversion by 64 % (down to∼ 33 Tg C month-1) in July and by 79 % (down to∼ 15 Tg C month-1) in December. When the results are integrated over the well-observed western European domain, the uncertainty reduction shows no seasonal variability. The effect of decreasing the correlation length of the prior uncertainty, or of reducing the transport model errors compared to their present configuration (when conducting real-data inversion cases) can be larger than that of the extension of the measurement network in areas where the 23 station observation network is the densest. We show that with a configuration of the ICOS atmospheric network containing 66 sites that can be expected on the long-term, the uncertainties in a 2-week-mean NEE will be reduced by up to 50–80 % for countries like Finland, Germany, France and Spain, which could significantly improvement (and at least a high complementarity to) our knowledge of NEE derived from biomass and soil carbon inventories at multi-annual scales.
This study presents two methods for estimating methane emissions from a waste water treatment plant (WWTP) along with results from a measurement campaign at a WWTP in Valence, France. These methods, ...chamber measurements and tracer release, rely on Fourier transform infrared spectroscopy and cavity ring-down spectroscopy instruments. We show that the tracer release method is suitable for quantifying facility- and some process-scale emissions, while the chamber measurements provide insight into individual process emissions. Uncertainties for the two methods are described and discussed. Applying the methods to CH4 emissions of the WWTP, we confirm that the open basins are not a major source of CH4 on the WWTP (about 10 % of the total emissions), but that the pretreatment and sludge treatment are the main emitters. Overall, the waste water treatment plant is representative of an average French WWTP.
The exchanges of carbon, water and energy between the atmosphere and the Amazon basin have global implications for the current and future climate. Here, the global atmospheric inversion system of the ...Monitoring of Atmospheric Composition and Climate (MACC) service is used to study the seasonal and interannual variations of biogenic CO2 fluxes in Amazonia during the period 2002-2010. The system assimilated surface measurements of atmospheric CO2 mole fractions made at more than 100 sites over the globe into an atmospheric transport model. The present study adds measurements from four surface stations located in tropical South America, a region poorly covered by CO2 observations. The estimates of net ecosystem exchange (NEE) optimized by the inversion are compared to an independent estimate of NEE upscaled from eddy-covariance flux measurements in Amazonia. They are also qualitatively evaluated against reports on the seasonal and interannual variations of the land sink in South America from the scientific literature. We attempt at assessing the impact on NEE of the strong droughts in 2005 and 2010 (due to severe and longer-than-usual dry seasons) and the extreme rainfall conditions registered in 2009. The spatial variations of the seasonal and interannual variability of optimized NEE are also investigated. While the inversion supports the assumption of strong spatial heterogeneity of these variations, the results reveal critical limitations of the coarse-resolution transport model, the surface observation network in South America during the recent years and the present knowledge of modelling uncertainties in South America that prevent our inversion from capturing the seasonal patterns of fluxes across Amazonia. However, some patterns from the inversion seem consistent with the anomaly of moisture conditions in 2009.
Assigning proper prior uncertainties for inverse modelling of CO2 is of high importance, both to regularise the otherwise ill-constrained inverse problem and to quantitatively characterise the ...magnitude and structure of the error between prior and "true" flux. We use surface fluxes derived from three biosphere models – VPRM, ORCHIDEE, and 5PM – and compare them against daily averaged fluxes from 53 eddy covariance sites across Europe for the year 2007 and against repeated aircraft flux measurements encompassing spatial transects. In addition we create synthetic observations using modelled fluxes instead of the observed ones to explore the potential to infer prior uncertainties from model–model residuals. To ensure the realism of the synthetic data analysis, a random measurement noise was added to the modelled tower fluxes which were used as reference. The temporal autocorrelation time for tower model–data residuals was found to be around 30 days for both VPRM and ORCHIDEE but significantly different for the 5PM model with 70 days. This difference is caused by a few sites with large biases between the data and the 5PM model. The spatial correlation of the model–data residuals for all models was found to be very short, up to few tens of kilometres but with uncertainties up to 100 % of this estimation. Propagating this error structure to annual continental scale yields an uncertainty of 0.06 Gt C and strongly underestimates uncertainties typically used from atmospheric inversion systems, revealing another potential source of errors. Long spatial e-folding correlation lengths up to several hundreds of kilometres were determined when synthetic data were used. Results from repeated aircraft transects in south-western France are consistent with those obtained from the tower sites in terms of spatial autocorrelation (35 km on average) while temporal autocorrelation is markedly lower (13 days). Our findings suggest that the different prior models have a common temporal error structure. Separating the analysis of the statistics for the model data residuals by seasons did not result in any significant differences of the spatial e-folding correlation lengths.
► 4DVAR data assimilation is used to adjust ocean surface forcing and initial state. ► SSH, SST, and in situ data are assimilated in the California Current System. ► Corrections to surface forcing ...are needed to reduce subsurface temperature errors. ► Corrections to surface forcing improve systematically the ocean state analyses. ► Abnormal corrections to surface forcing may arise due to the effects of model error.
The option for surface forcing correction, recently developed in the 4D-variational (4DVAR) data assimilation systems of the Regional Ocean Model System (ROMS), is presented. Assimilation of remotely-sensed (satellite sea surface height anomaly and sea surface temperature) and in situ (from mechanical and expendable bathythermographs, Argo floats and CTD profiles) oceanic observations has been applied in a realistic, high resolution configuration of the California Current System (CCS) to sequentially correct model initial conditions and surface forcing, using the Incremental Strong constraint version of ROMS-4DVAR (ROMS-IS4DVAR). Results from both twin and real data experiments are presented where it is demonstrated that ROMS-IS4DVAR always reduces the difference between the model and the observations that are assimilated. However, without corrections to the surface forcing, the assimilation of surface data can degrade the temperature structure at depth. When using surface forcing adjustment in ROMS-IS4DVAR the system does not degrade the temperature structure at depth, because differences between the model and surface observations can be reduced through corrections to surface forcing rather than to temperature at depth. However, corrections to surface forcing can generate abnormal spatial and temporal variability in the structure of the wind stress or surface heat flux fields if not properly constrained. This behavior can be partially controlled via the choice of decorrelation length scales that are assumed for the forcing errors. Abnormal forcing corrections may also arise due to the effects of model error which are not accounted for in IS4DVAR. In particular, data assimilation tends to weaken the alongshore wind stress in an attempt to reduce the rate of coastal upwelling, which seems to be too strong due to other sources of error. However, corrections to wind stress and surface heat flux improve systematically the ocean state analyses. Trends in the correction of surface heat fluxes indicate that, given the ocean model used and its potential limitations, the heat flux data from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) used to impose surface conditions in the model are generally too low except in spring-summer, in the upwelling region, where they are too high. Comparisons with independent data provide confidence in the resulting forecast ocean circulation on timescales ∼14
days, with less than 1.5
°C, 0.3
psu, and 9
cm RMS error in temperature, salinity and sea surface height anomaly, respectively, compared to observations.
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