Atmospheric CO2 Fluxes and Soil Respiration Measurements over Sugarcane in Southeast Brazil

2019 ◽  
pp. 405-414 ◽  
Author(s):  
H.R. Da Rocha ◽  
O.M.R. Cabral ◽  
M.A.F. Da Silva Dias ◽  
M.A. Ligo ◽  
J.A. Elbers ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Southeast Brazil

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Atmospheric CO2 measurements and error analysis on seasonal air–sea CO2 fluxes in the Bay of Biscay

2007 ◽  
Vol 66 (1-4) ◽  
pp. 285-296 ◽  
Author(s):  
X.A. Padin ◽  
M. Vázquez-Rodríguez ◽  
A.F. Rios ◽  
F.F. Pérez
Keyword(s):  
Error Analysis ◽  
Atmospheric Co2 ◽  
Bay Of Biscay ◽  
Co2 Fluxes

scholarly journals Improved constraints on northern extratropical CO2 fluxes obtained by combining surface-based and space-based atmospheric CO2 measurements

2019 ◽  
Author(s):  
Brendan Keith Aidan Byrne ◽  
Junjie Liu ◽  
Meemong Lee ◽  
Ian T. Baker ◽  
Kevin W. Bowman ◽  
...  
Keyword(s):  
Atmospheric Co2 ◽  
Co2 Fluxes

scholarly journals Atmospheric CO2 fluxes in a highly polluted estuary (the Scheldt)

1996 ◽  
Vol 41 (2) ◽  
pp. 365-369 ◽  
Author(s):  
Michel Frankignoulle ◽  
Isabelle Bourge ◽  
Roland Wollast
Keyword(s):  
Atmospheric Co2 ◽  
Co2 Fluxes

Analysis of atmospheric CO2 growth rates at Mauna Loa using CO2 fluxes derived from an inverse model

Tellus B ◽  
2005 ◽  
Vol 57 (5) ◽  
pp. 357-365 ◽  
Author(s):  
PRABIR K. PATRA ◽  
SHAMIL MAKSYUTOV ◽  
TAKAKIYO NAKAZAWA
Keyword(s):  
Growth Rates ◽  
Inverse Model ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Mauna Loa

scholarly journals Natural Variability in a Stable, 1000-Yr Global Coupled Climate–Carbon Cycle Simulation

2006 ◽  
Vol 19 (13) ◽  
pp. 3033-3054 ◽  
Author(s):  
Scott C. Doney ◽  
Keith Lindsay ◽  
Inez Fung ◽  
Jasmin John
Keyword(s):  
Carbon Cycle ◽  
Surface Temperature ◽  
Time Scale ◽  
Co2 Flux ◽  
Natural Variability ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Climate System Model ◽  
Control Simulation ◽  
Ocean Carbon

Abstract A new 3D global coupled carbon–climate model is presented in the framework of the Community Climate System Model (CSM-1.4). The biogeochemical module includes explicit land water–carbon coupling, dynamic carbon allocation to leaf, root, and wood, prognostic leaf phenology, multiple soil and detrital carbon pools, oceanic iron limitation, a full ocean iron cycle, and 3D atmospheric CO2 transport. A sequential spinup strategy is utilized to minimize the coupling shock and drifts in land and ocean carbon inventories. A stable, 1000-yr control simulation [global annual mean surface temperature ±0.10 K and atmospheric CO2 ± 1.2 ppm (1σ)] is presented with no flux adjustment in either physics or biogeochemistry. The control simulation compares reasonably well against observations for key annual mean and seasonal carbon cycle metrics; regional biases in coupled model physics, however, propagate clearly into biogeochemical error patterns. Simulated interannual-to-centennial variability in atmospheric CO2 is dominated by terrestrial carbon flux variability, ±0.69 Pg C yr−1 (1σ global net annual mean), which in turn reflects primarily regional changes in net primary production modulated by moisture stress. Power spectra of global CO2 fluxes are white on time scales beyond a few years, and thus most of the variance is concentrated at high frequencies (time scale &lt;4 yr). Model variability in air–sea CO2 fluxes, ±0.10 Pg C yr−1 (1σ global annual mean), is generated by variability in sea surface temperature, wind speed, export production, and mixing/upwelling. At low frequencies (time scale &gt;20 yr), global net ocean CO2 flux is strongly anticorrelated (0.7–0.95) with the net CO2 flux from land; the ocean tends to damp (20%–25%) slow variations in atmospheric CO2 generated by the terrestrial biosphere. The intrinsic, unforced natural variability in land and ocean carbon storage is the “noise” that complicates the detection and mechanistic attribution of contemporary anthropogenic carbon sinks.

scholarly journals Forecasting global atmospheric CO<sub>2</sub>

2014 ◽  
Vol 14 (9) ◽  
pp. 13909-13962 ◽  
Author(s):  
A. Agustí-Panareda ◽  
S. Massart ◽  
F. Chevallier ◽  
S. Boussetta ◽  
G. Balsamo ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Real Time ◽  
Diurnal Cycle ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Physical Models ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Near Surface ◽  
Forecasting System

Abstract. A new global atmospheric carbon dioxide (CO2) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO2 forecasting system is that the land surface, including vegetation CO2 fluxes, is modelled online within the IFS. Other CO2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO2 fluxes also lead to accumulating errors in the CO2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO2 fluxes compared to total optimized fluxes and the atmospheric CO2 compared to observations. The largest biases in the atmospheric CO2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO2 analyses based on the assimilation of CO2 satellite retrievals, as they become available in near-real time. In this way, the accumulation of errors in the atmospheric CO2 forecast will be reduced. Improvements in the CO2 forecast are also expected with the continuous developments in the operational IFS.

scholarly journals Seasonal variations of sea–air CO<sub>2</sub> fluxes in the largest tropical marginal sea (South China Sea) based on multiple-year underway measurements

2013 ◽  
Vol 10 (11) ◽  
pp. 7775-7791 ◽  
Author(s):  
W.-D. Zhai ◽  
M.-H. Dai ◽  
B.-S. Chen ◽  
X.-H. Guo ◽  
Q. Li ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
River Estuary ◽  
Co2 Flux ◽  
Pearl River Estuary ◽  
Sea Surface ◽  
Atmospheric Co2 ◽  
Pearl River ◽  
Co2 Fluxes ◽  
The Pearl River

Abstract. Based upon 14 field surveys conducted between 2003 and 2008, we showed that the seasonal pattern of sea surface partial pressure of CO2 (pCO2) and sea–air CO2 fluxes differed among four different physical–biogeochemical domains in the South China Sea (SCS) proper. The four domains were located between 7 and 23° N and 110 and 121° E, covering a surface area of 1344 × 103 km2 and accounting for ~ 54% of the SCS proper. In the area off the Pearl River estuary, relatively low pCO2 values of 320 to 390 μatm were observed in all four seasons and both the biological productivity and CO2 uptake were enhanced in summer in the Pearl River plume waters. In the northern SCS slope/basin area, a typical seasonal cycle of relatively high pCO2 in the warm seasons and relatively low pCO2 in the cold seasons was revealed. In the central/southern SCS area, moderately high sea surface pCO2 values of 360 to 425 μatm were observed throughout the year. In the area west of the Luzon Strait, a major exchange pathway between the SCS and the Pacific Ocean, pCO2 was particularly dynamic in winter, when northeast monsoon induced upwelling events and strong outgassing of CO2. These episodic events might have dominated the annual sea–air CO2 flux in this particular area. The estimate of annual sea–air CO2 fluxes showed that most areas of the SCS proper served as weak to moderate sources of the atmospheric CO2, with sea–air CO2 flux values of 0.46 ± 0.43 mol m−2 yr−1 in the northern SCS slope/basin, 1.37 ± 0.55 mol m−2 yr−1 in the central/southern SCS, and 1.21 ± 1.48 mol m−2 yr−1 in the area west of the Luzon Strait. However, the annual sea–air CO2 exchange was nearly in equilibrium (−0.44 ± 0.65 mol m−2 yr−1) in the area off the Pearl River estuary. Overall the four domains contributed (18 ± 10) × 1012 g C yr−1 to the atmospheric CO2.

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