European CO2 fluxes from atmospheric inversions using regional and global transport models

2010 ◽  
pp. 93-115 ◽  
Author(s):  
L. Rivier ◽  
◽  
Ph. Peylin ◽  
Ph. Ciais ◽  
M. Gloor ◽  
...  
2010 ◽  
Vol 103 (1-2) ◽  
pp. 93-115 ◽  
Author(s):  
L. Rivier ◽  
◽  
Ph. Peylin ◽  
Ph. Ciais ◽  
M. Gloor ◽  
...  

2010 ◽  
Vol 103 (1-2) ◽  
pp. 69-92 ◽  
Author(s):  
P. Ciais ◽  
P. Rayner ◽  
F. Chevallier ◽  
P. Bousquet ◽  
M. Logan ◽  
...  

2019 ◽  
Vol 19 (5) ◽  
pp. 2991-3006 ◽  
Author(s):  
Kieran Brophy ◽  
Heather Graven ◽  
Alistair J. Manning ◽  
Emily White ◽  
Tim Arnold ◽  
...  

Abstract. Atmospheric inverse modelling has become an increasingly useful tool for evaluating emissions of greenhouse gases including methane, nitrous oxide, and synthetic gases such as hydrofluorocarbons (HFCs). Atmospheric inversions for emissions of CO2 from fossil fuel combustion (ffCO2) are currently being developed. The aim of this paper is to investigate potential errors and uncertainties related to the spatial and temporal prior representation of emissions and modelled atmospheric transport for the inversion of ffCO2 emissions in the US state of California. We perform simulation experiments based on a network of ground-based observations of CO2 concentration and radiocarbon in CO2 (a tracer of ffCO2), combining prior (bottom-up) emission models and transport models currently used in many atmospheric studies. The potential effect of errors in the spatial and temporal distribution of prior emission estimates is investigated in experiments by using perturbed versions of the emission estimates used to create the pseudo-data. The potential effect of transport error was investigated by using three different atmospheric transport models for the prior and pseudo-data simulations. We find that the magnitude of biases in posterior total state emissions arising from errors in the spatial and temporal distribution in prior emissions in these experiments are 1 %–15 % of posterior total state emissions and are generally smaller than the 2σ uncertainty in posterior emissions. Transport error in these experiments introduces biases of −10 % to +6 % into posterior total state emissions. Our results indicate that uncertainties in posterior total state ffCO2 estimates arising from the choice of prior emissions or atmospheric transport model are on the order of 15 % or less for the ground-based network in California we consider. We highlight the need for temporal variations to be included in prior emissions and for continuing efforts to evaluate and improve the representation of atmospheric transport for regional ffCO2 inversions.


2018 ◽  
Author(s):  
Kieran Brophy ◽  
Heather Graven ◽  
Alistair J. Manning ◽  
Emily White ◽  
Tim Arnold ◽  
...  

Abstract. Atmospheric inverse modelling has become an increasingly useful tool for evaluating emissions of greenhouse gases including methane, nitrous oxide and synthetic gases such as hydrofluorocarbons (HFCs). Atmospheric inversions for emissions of CO2 from fossil fuel combustion (ffCO2) are currently being developed. The aim of this paper is to investigate potential errors and uncertainties related to the spatial and temporal prior representation of emissions and modelled atmospheric transport for the inversion of ffCO2 emissions in the U.S. state of California. We perform simulation experiments based on a network of ground-based observations of CO2 concentration and radiocarbon in CO2 (a tracer of ffCO2), combining prior (bottom-up) emission models and transport models currently used in many atmospheric studies. The potential effect of errors in the spatial and temporal distribution of prior emission estimates is investigated in experiments by using perturbed versions of the emissions estimates used to create the pseudo data. The potential effect of transport error was investigated by using three different atmospheric transport models for the prior and pseudo data simulations. We find that the magnitude of biases in posterior state-total emissions arising from errors in the spatial and temporal distribution in prior emissions in these experiments are 1–15 % of posterior state-total emissions, and generally smaller than the 2-σ uncertainty in posterior emissions. Transport error in these experiments introduces biases of −10 % to +6 % in posterior state-total emissions. Our results indicate that uncertainties in posterior state-total ffCO2 estimates arising from the choice of prior emissions or atmospheric transport model are on the order of 15 % or less for the ground-based network in California we consider. We highlight the need for temporal variations to be included in prior emissions, and for continuing efforts to evaluate and improve the representation of atmospheric transport for regional ffCO2 inversions.


2010 ◽  
pp. 69-92 ◽  
Author(s):  
P. Ciais ◽  
P. Rayner ◽  
F. Chevallier ◽  
P. Bousquet ◽  
M. Logan ◽  
...  

2013 ◽  
Vol 10 (11) ◽  
pp. 7035-7052 ◽  
Author(s):  
V. V. S. S. Sarma ◽  
A. Lenton ◽  
R. M. Law ◽  
N. Metzl ◽  
P. K. Patra ◽  
...  

Abstract. The Indian Ocean (44° S–30° N) plays an important role in the global carbon cycle, yet it remains one of the most poorly sampled ocean regions. Several approaches have been used to estimate net sea–air CO2 fluxes in this region: interpolated observations, ocean biogeochemical models, atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Indian Ocean sea–air CO2 fluxes between 1990 and 2009. Using all of the models and inversions, the median annual mean sea–air CO2 uptake of −0.37 ± 0.06 PgC yr−1 is consistent with the −0.24 ± 0.12 PgC yr−1 calculated from observations. The fluxes from the southern Indian Ocean (18–44° S; −0.43 ± 0.07 PgC yr−1 are similar in magnitude to the annual uptake for the entire Indian Ocean. All models capture the observed pattern of fluxes in the Indian Ocean with the following exceptions: underestimation of upwelling fluxes in the northwestern region (off Oman and Somalia), overestimation in the northeastern region (Bay of Bengal) and underestimation of the CO2 sink in the subtropical convergence zone. These differences were mainly driven by lack of atmospheric CO2 data in atmospheric inversions, and poor simulation of monsoonal currents and freshwater discharge in ocean biogeochemical models. Overall, the models and inversions do capture the phase of the observed seasonality for the entire Indian Ocean but overestimate the magnitude. The predicted sea–air CO2 fluxes by ocean biogeochemical models (OBGMs) respond to seasonal variability with strong phase lags with reference to climatological CO2 flux, whereas the atmospheric inversions predicted an order of magnitude higher seasonal flux than OBGMs. The simulated interannual variability by the OBGMs is weaker than that found by atmospheric inversions. Prediction of such weak interannual variability in CO2 fluxes by atmospheric inversions was mainly caused by a lack of atmospheric data in the Indian Ocean. The OBGM models suggest a small strengthening of the sink over the period 1990–2009 of −0.01 PgC decade−1. This is inconsistent with the observations in the southwestern Indian Ocean that shows the growth rate of oceanic pCO2 was faster than the observed atmospheric CO2 growth, a finding attributed to the trend of the Southern Annular Mode (SAM) during the 1990s.


2010 ◽  
Vol 3 (2) ◽  
pp. 855-888 ◽  
Author(s):  
S. R. Freitas ◽  
K. M. Longo ◽  
M. F. Alonso ◽  
M. Pirre ◽  
V. Marecal ◽  
...  

Abstract. The pre-processor PREP-CHEM-SRC presented in the paper is a comprehensive tool aiming at preparing emissions fields of trace gases and aerosols for use in regional or global transport models. The emissions considered are urban/industrial, biogenic, biomass burning, volcanic, biofuel use and burning from agricultural waste sources from most recent databases or from satellite fire detections for biomass burning. A plumerise model is used to derive the height of smoke emissions from satellite fire products. The pre-processor provides emission fields interpolated onto the transport model grid. Several map projections can be chosen. The way to include these emissions in transport models is also detailed. The pre-processor is coded using Fortran 90 and C and is driven by a namelist allowing the user to choose the type of emissions and the database.


2018 ◽  
Vol 18 (10) ◽  
pp. 7189-7215 ◽  
Author(s):  
Sourish Basu ◽  
David F. Baker ◽  
Frédéric Chevallier ◽  
Prabir K. Patra ◽  
Junjie Liu ◽  
...  

Abstract. We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions.


2013 ◽  
Vol 10 (6) ◽  
pp. 4037-4054 ◽  
Author(s):  
A. Lenton ◽  
B. Tilbrook ◽  
R. M. Law ◽  
D. Bakker ◽  
S. C. Doney ◽  
...  

Abstract. The Southern Ocean (44–75° S) plays a critical role in the global carbon cycle, yet remains one of the most poorly sampled ocean regions. Different approaches have been used to estimate sea–air CO2 fluxes in this region: synthesis of surface ocean observations, ocean biogeochemical models, and atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Southern Ocean sea–air CO2 fluxes between 1990–2009. Using all models and inversions (26), the integrated median annual sea–air CO2 flux of −0.42 ± 0.07 Pg C yr−1 for the 44–75° S region, is consistent with the −0.27 ± 0.13 Pg C yr−1 calculated using surface observations. The circumpolar region south of 58° S has a small net annual flux (model and inversion median: −0.04 ± 0.07 Pg C yr−1 and observations: +0.04 ± 0.02 Pg C yr−1), with most of the net annual flux located in the 44 to 58° S circumpolar band (model and inversion median: −0.36 ± 0.09 Pg C yr−1 and observations: −0.35 ± 0.09 Pg C yr−1). Seasonally, in the 44–58° S region, the median of 5 ocean biogeochemical models captures the observed sea–air CO2 flux seasonal cycle, while the median of 11 atmospheric inversions shows little seasonal change in the net flux. South of 58° S, neither atmospheric inversions nor ocean biogeochemical models reproduce the phase and amplitude of the observed seasonal sea–air CO2 flux, particularly in the Austral Winter. Importantly, no individual atmospheric inversion or ocean biogeochemical model is capable of reproducing both the observed annual mean uptake and the observed seasonal cycle. This raises concerns about projecting future changes in Southern Ocean CO2 fluxes. The median interannual variability from atmospheric inversions and ocean biogeochemical models is substantial in the Southern Ocean; up to 25% of the annual mean flux, with 25% of this interannual variability attributed to the region south of 58° S. Resolving long-term trends is difficult due to the large interannual variability and short time frame (1990–2009) of this study; this is particularly evident from the large spread in trends from inversions and ocean biogeochemical models. Nevertheless, in the period 1990–2009 ocean biogeochemical models do show increasing oceanic uptake consistent with the expected increase of −0.05 Pg C yr−1 decade−1. In contrast, atmospheric inversions suggest little change in the strength of the CO2 sink broadly consistent with the results of Le Quéré et al. (2007).


2013 ◽  
Vol 10 (7) ◽  
pp. 10759-10810
Author(s):  
V. V. S. S. Sarma ◽  
A. Lenton ◽  
R. Law ◽  
N. Metzl ◽  
P. K. Patra ◽  
...  

Abstract. The Indian Ocean (44° S–30° N) plays an important role in the global carbon cycle, yet remains one of the most poorly sampled ocean regions. Several approaches have been used to estimate net sea–air CO2 fluxes in this region: interpolated observations, ocean biogeochemical models, atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Indian Ocean sea–air CO2 fluxes between 1990 and 2009. Using all of the models and inversions, the median annual mean sea–air CO2 uptake of −0.37 ± 0.06 Pg C yr–1, is consistent with the −0.24 ± 0.12 Pg C yr–1 calculated from observations. The fluxes from the Southern Indian Ocean (18° S–44° S; −0.43 ± 0.07 Pg C yr–1) are similar in magnitude to the annual uptake for the entire Indian Ocean. All models capture the observed pattern of fluxes in the Indian Ocean with the following exceptions: underestimation of upwelling fluxes in the northwestern region (off Oman and Somalia), over estimation in the northeastern region (Bay of Bengal) and underestimation of the CO2 sink in the subtropical convergence zone. These differences were mainly driven by a lack of atmospheric CO2 data in atmospheric inversions, and poor simulation of monsoonal currents and freshwater discharge in ocean biogeochemical models. Overall, the models and inversions do capture the phase of the observed seasonality for the entire Indian Ocean but over estimate the magnitude. The predicted sea–air CO2 fluxes by Ocean BioGeochemical Models (OBGM) respond to seasonal variability with strong phase lags with reference to climatological CO2 flux, whereas the atmospheric inversions predict an order of magnitude higher seasonal flux than OBGMs. The simulated interannual variability by the OBGMs is weaker than atmospheric inversions. Prediction of such weak interannual variability in CO2 fluxes by atmospheric inversions was mainly caused by lack of atmospheric data in the Indian Ocean. The OBGM models suggest a small strengthening of the sink over the period 1990–2009 of −0.01 Pg C decade–1. This is inconsistent with the observations in the southwest Indian Ocean that shows the growth rate of oceanic pCO2 was faster than the observed atmospheric CO2 growth, a finding attributed to the trend of the Southern Annual Mode (SAM) during the 1990s.


Sign in / Sign up

Export Citation Format

Share Document