CSIRO High-precision Measurement of Atmospheric CO2 Concentration in Australia. Part 2: Cape Grim, Surface CO2 Measurements and Carbon Cycle Modelling

2017 ◽  
Vol 28 (2) ◽  
pp. 126 ◽  
Author(s):  
Graeme I. Pearman ◽  
Paul J. Fraser ◽  
John R. Garratt
Keyword(s):  
Carbon Cycle ◽  
High Precision ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Political Activity ◽  
High Precision Measurement ◽  
Aerosol Composition ◽  
North West ◽  
Research Activities ◽  
Cape Grim

A companion paper discusses the history of, and rationale for, the development of a CSIRO programme of atmospheric carbondioxide (CO2) concentration measurements in Australia based on aircraft air sampling, field and laboratory measurements.1 Here, we describe parallel efforts to establish a permanent, ground-based atmospheric Baseline Station at Cape Grim, north-west Tasmania, the political activity required for its establishment, and the work undertaken to select a site commensurate with its long-term objectives. Additional CO2 measurements undertaken to complement the aircraft and Cape Grim measurements are discussed. The development of the Australian Baseline Station was part of an emerging international effort to obtain high-precision measurements of trace gas and aerosol composition of the atmosphere, and to quantify any changes in composition that might be occurring and their possible impact on global climate.We discuss the early development of global carbon cycle models, including the representations of atmospheric transport, and the interpretation of modern atmospheric CO2 data and historic air samples encapsulated in Antarctic ice and firn. The accumulated knowledge from these research activities, together with that collected by international colleagues, forms the basis of our understanding of changes occurring in CO2 concentration. It has contributed to an understanding of the mechanisms of the past and present biogeochemical cycling of CO2, providing predictions of future changes in CO2 concentration.

CSIRO High-precision Measurement of Atmospheric CO2 Concentration in Australia. Part 1: Initial Motivation, Techniques and Aircraft Sampling

2017 ◽  
Vol 28 (2) ◽  
pp. 111 ◽  
Author(s):  
Graeme I. Pearman ◽  
John R. Garratt ◽  
Paul J. Fraser
Keyword(s):  
High Precision ◽  
Precision Measurement ◽  
Measurement Techniques ◽  
Ice Cores ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
High Precision Measurement ◽  
Concentration Data ◽  
Atmospheric Co2 Concentration ◽  
The Impact

The potential for carbon dioxide (CO2) in the atmosphere to influence global surface temperatures was first recognized in the mid-nineteenth century. Even so, high-precision measurements of atmospheric CO2 concentration were not commenced until the International Geophysical Year (1957–8), following concerns of the climatic impact of increased use of fossil fuels and the concomitant release of CO2 into the atmosphere. In Australia, an early (1960s–70s) interest in the high-precision measurement of CO2 concentration was stimulated by a study of the photosynthesis and respiration of awheat crop. This study conducted in north-easternVictoria during 19717–2 led two young CSIRO scientists, J. R. Garratt and G. I. Pearman, encouraged by their Chief, C. H. B. Priestley, to extend micro-environment CO2 studies to larger-scale measurements of CO2 concentration in the background atmosphere. The significant extension of the observation programme required refined measurement techniques to improve both the precision and absolute comparability with observations made by laboratories overseas. Joined in 1974 by P. J. Fraser, they identified the impact of pressure broadening on calibration techniques used in the non-dispersive infrared absorption method of CO2 concentration measurement. This, in turn, led to improved inter-comparability of CO2 concentration data collected around the globe. Acomprehensive aircraft-based air sampling programmewas established in the early 1970s, leading to increased understanding of the time and space variability of CO2 concentration throughout the depth of the troposphere and lower stratosphere in the mid-latitudes of the Southern Hemisphere. In turn this led to: (i) the establishment of a permanent ground-based observatory at Cape Grim, north-western Tasmania; (ii) the development of carbon cycle models; and (iii) measurements of 12CO2, 13CO2 and 14CO2 relative abundances in current and past atmospheres, the last from air samples trapped in ice cores (described in Part 2, the companion paper). The accumulated data from these studies, together with those collected by international colleagues, form the basis of our understanding of the changes of CO2 concentration over thousands of years. In addition, the data have contributed to our understanding of the mechanisms of past and present biogeochemical cycling of CO2 that provides the predictive basis for future changes in CO2 concentration.

scholarly journals Quantifying Carbon Cycle Feedbacks

2009 ◽  
Vol 22 (19) ◽  
pp. 5232-5250 ◽  
Author(s):  
J. M. Gregory ◽  
C. D. Jones ◽  
P. Cadule ◽  
P. Friedlingstein
Keyword(s):  
Carbon Cycle ◽  
Co2 Emissions ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Feedback ◽  
Climate Response ◽  
Carbon Cycle Model ◽  
Atmospheric Co2 Concentration ◽  
Intercomparison Project ◽  
Earth System Models

Abstract Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration–carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate–carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), climate–carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration–carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate–carbon feedback and more uncertain. The concentration–carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon–climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration–carbon and climate–carbon feedbacks do not combine linearly, and the concentration–carbon feedback is dependent on scenario and time.

scholarly journals Comment on "Carbon farming in hot, dry coastal areas: an option for climate change mitigation" by Becker et al. (2013)

2013 ◽  
Vol 4 (2) ◽  
pp. 869-873
Author(s):  
M. Heimann
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Reference Scenario ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Sink ◽  
Carbon Dioxide Co2 ◽  
Global Carbon

Abstract. Becker et al. (2013) argue that an afforestation of 0.73 109 ha with Jatropha curcas plants would generate an additional terrestrial carbon sink of 4.3 PgC yr−1, enough to stabilise the atmospheric mixing ratio of carbon dioxide (CO2) at current levels. However, this is not consistent with the dynamics of the global carbon cycle. Using a well established global carbon cycle model, the effect of adding such a hypothetical sink leads to a reduction of atmospheric CO2 levels in the year 2030 by 25 ppm compared to a reference scenario. However, the stabilisation of the atmospheric CO2 concentration requires a much larger additional sink or corresponding reduction of anthropogenic emissions.

scholarly journals Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2362 ◽  
Author(s):  
Chengzhi Xiang ◽  
Ge Han ◽  
Yuxin Zheng ◽  
Xin Ma ◽  
Wei Gong
Keyword(s):  
Wavelet Transform ◽  
Carbon Cycle ◽  
Signal To Noise Ratio ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Signal Denoising ◽  
Differential Absorption ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Lifting Wavelet

Atmospheric CO2 plays an important role in controlling climate change and its effect on the carbon cycle. However, detailed information on the dynamics of CO2 vertical mixing remains lacking, which hinders the accurate understanding of certain key features of the carbon cycle. Differential absorption lidar (DIAL) is a promising technology for CO2 detection due to its characteristics of high precision, high time resolution, and high spatial resolution. Ground-based CO2-DIAL can provide the continuous observations of the vertical profile of CO2 concentration, which can be highly significant to gaining deeper insights into the rectification effect of CO2, the ratio of respiration photosynthesis, and the CO2 dome in urban areas. A set of ground-based CO2-DIAL systems were developed by our team and highly accurate long-term laboratory experiments were conducted. Nonetheless, the performance suffered from low signal-to-noise ratio (SNR) in field explorations because of decreasing aerosol concentrations with increasing altitude and surrounding interference according to the results of our experiments in Wuhan and Huainan. The concentration of atmospheric CO2 is derived from the difference of signals between on-line and off-line wavelengths; thus, low SNR will cause the superimposition of the final inversion error. In such a situation, an efficient and accurate denoising algorithm is critical for a ground-based CO2-DIAL system, particularly in field experiments. In this study, a method based on lifting wavelet transform (LWT) for CO2-DIAL signal denoising was proposed. This method, which is an improvement of the traditional wavelet transform, can select different predictive and update functions according to the characteristics of lidar signals, thereby making it suitable for the signal denoising of CO2-DIAL. Experiment analyses were conducted to evaluate the denoising effect of LWT. For comparison, ensemble empirical mode decomposition denoising was also performed on the same lidar signal. In addition, this study calculated the coefficient of variation (CV) at the same altitude among multiple original signals within 10 min and then performed the same calculation on the denoised signal. Finally, high-quality signal of ground-based CO2-DIAL was obtained using the LWT denoising method. The differential absorption optical depths of the denoised signals obtained via LWT were calculated, and the profile distribution information of CO2 concentration was acquired during field detection by using our developed CO2-DIAL systems.

scholarly journals The global carbon budget 1959–2011

2013 ◽  
Vol 5 (1) ◽  
pp. 165-185 ◽  
Author(s):  
C. Le Quéré ◽  
R. J. Andres ◽  
T. Boden ◽  
T. Conway ◽  
R. A. Houghton ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Carbon Cycle ◽  
Co2 Emissions ◽  
Carbon Budget ◽  
Co2 Concentration ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Global Carbon Budget ◽  
Atmospheric Co2 Concentration ◽  
Global Carbon

Abstract. Accurate assessments of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics, while emissions from Land-Use Change (ELUC), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms. For the last decade available (2002–2011), EFF was 8.3 ± 0.4 PgC yr−1, ELUC 1.0 ± 0.5 PgC yr−1, GATM 4.3 ± 0.1 PgC yr−1, SOCEAN 2.5 ± 0.5 PgC yr−1, and SLAND 2.6 ± 0.8 PgC yr−1. For year 2011 alone, EFF was 9.5 ± 0.5 PgC yr−1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 ± 0.5 PgC yr−1, approximately constant throughout the decade; GATM was 3.6 ± 0.2 PgC yr−1, SOCEAN was 2.7 ± 0.5 PgC yr−1, and SLAND was 4.1 ± 0.9 PgC yr−1. GATM was low in 2011 compared to the 2002–2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Niña conditions in the Pacific Ocean. The global atmospheric CO2 concentration reached 391.31 ± 0.13 ppm at the end of year 2011. We estimate that EFF will have increased by 2.6% (1.9–3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future. All data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_V2013). Global carbon budget 2013

scholarly journals What was the source of the atmospheric CO<sub>2</sub> increase during the Holocene?

2019 ◽  
Vol 16 (13) ◽  
pp. 2543-2555 ◽  
Author(s):  
Victor Brovkin ◽  
Stephan Lorenz ◽  
Thomas Raddatz ◽  
Tatiana Ilyina ◽  
Irene Stemmler ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Ice Core ◽  
Carbon Sources ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Ion Concentration ◽  
Model Framework ◽  
Atmospheric Co2 Concentration ◽  
Transient Simulations

Abstract. The atmospheric CO2 concentration increased by about 20 ppm from 6000 BCE to the pre-industrial period (1850 CE). Several hypotheses have been proposed to explain mechanisms of this CO2 growth based on either ocean or land carbon sources. Here, we apply the Earth system model MPI-ESM-LR for two transient simulations of climate and carbon cycle dynamics during this period. In the first simulation, atmospheric CO2 is prescribed following ice-core CO2 data. In response to the growing atmospheric CO2 concentration, land carbon storage increases until 2000 BCE, stagnates afterwards, and decreases from 1 CE, while the ocean continuously takes CO2 out of the atmosphere after 4000 BCE. This leads to a missing source of 166 Pg of carbon in the ocean–land–atmosphere system by the end of the simulation. In the second experiment, we applied a CO2 nudging technique using surface alkalinity forcing to follow the reconstructed CO2 concentration while keeping the carbon cycle interactive. In that case the ocean is a source of CO2 from 6000 to 2000 BCE due to a decrease in the surface ocean alkalinity. In the prescribed CO2 simulation, surface alkalinity declines as well. However, it is not sufficient to turn the ocean into a CO2 source. The carbonate ion concentration in the deep Atlantic decreases in both the prescribed and the interactive CO2 simulations, while the magnitude of the decrease in the prescribed CO2 experiment is underestimated in comparison with available proxies. As the land serves as a carbon sink until 2000 BCE due to natural carbon cycle processes in both experiments, the missing source of carbon for land and atmosphere can only be attributed to the ocean. Within our model framework, an additional mechanism, such as surface alkalinity decrease, for example due to unaccounted for carbonate accumulation processes on shelves, is required for consistency with ice-core CO2 data. Consequently, our simulations support the hypothesis that the ocean was a source of CO2 until the late Holocene when anthropogenic CO2 sources started to affect atmospheric CO2.

scholarly journals Limitations of the 1 % experiment as the benchmark idealized experiment for carbon cycle intercomparison in C<sup>4</sup>MIP

2019 ◽  
Vol 12 (2) ◽  
pp. 597-611 ◽  
Author(s):  
Andrew Hugh MacDougall
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Smooth Transition ◽  
Model Intercomparison ◽  
Carbon Cycle Model ◽  
Atmospheric Co2 Concentration ◽  
Benchmark Experiment ◽  
Intercomparison Project

Abstract. Idealized climate change simulations are used as benchmark experiments to facilitate the comparison of ensembles of climate models. In the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the 1 % per yearly compounded change in atmospheric CO2 concentration experiment was used to compare Earth system models with full representations of the global carbon cycle in the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP). However, this “1 % experiment” was never intended for such a purpose and implies a rise in atmospheric CO2 concentration at double the rate of the instrumental record. Here, we examine this choice by using an intermediate complexity climate model to compare the 1 % experiment to an idealized CO2 pathway derived from a logistic function. The comparison shows three key differences in model output when forcing the model with the logistic experiment. (1) The model forced with the logistic experiment exhibits a transition of the land biosphere from a carbon sink to a carbon source, a feature absent when forcing the model with the 1 % experiment. (2) The ocean uptake of carbon comes to dominate the carbon cycle as emissions decline, a feature that cannot be captured when forcing a model with the 1 % experiment, as emissions always increase in that experiment. (3) The permafrost carbon feedback to climate change under the 1 % experiment forcing is less than half the strength of the feedback seen under logistic experiment forcing. Using the logistic experiment also allows smooth transition to zero or negative emissions states, allowing these states to be examined without sharp discontinuities in CO2 emissions. The protocol for the CMIP6 iteration of C4MIP again sets the 1 % experiment as the benchmark experiment for model intercomparison; however, clever use of the Tier 2 experiments may alleviate some of the limitations outlined here. Given the limitations of the 1 % experiment as the benchmark experiment for carbon cycle intercomparisons, adding a logistic or similar idealized experiment to the protocol of the CMIP7 iteration of C4MIP is recommended.

scholarly journals Comment on "Carbon farming in hot, dry coastal areas: an option for climate change mitigation" by Becker et al. (2013)

2014 ◽  
Vol 5 (1) ◽  
pp. 41-42 ◽  
Author(s):  
M. Heimann
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Reference Scenario ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Sink ◽  
Carbon Dioxide Co2 ◽  
Global Carbon

Abstract. Becker et al. (2013) argue that an afforestation of 0.73 × 109 ha with Jatropha curcas plants would generate an additional terrestrial carbon sink of 4.3 PgC yr−1, enough to stabilise the atmospheric mixing ratio of carbon dioxide (CO2) at current levels. However, this is not consistent with the dynamics of the global carbon cycle. Using a well-established global carbon cycle model, the effect of adding such a hypothetical sink leads to a reduction of atmospheric CO2 levels in the year 2030 by 25 ppm compared to a reference scenario. However, the stabilisation of the atmospheric CO2 concentration requires a much larger additional sink or corresponding reduction of anthropogenic emissions.

scholarly journals Characterization of lower-cost medium precision atmospheric CO<sub>2</sub> monitoring systems for urban areas using commercial NDIR sensors

2018 ◽  
Author(s):  
Emmanuel Arzoumanian ◽  
Felix R. Vogel ◽  
Ana Bastos ◽  
Bakhram Gaynullin ◽  
Olivier Laurent ◽  
...  
Keyword(s):  
High Precision ◽  
Urban Areas ◽  
Lower Cost ◽  
Co2 Concentration ◽  
Calibration Method ◽  
Atmospheric Co2 ◽  
Precision Instrument ◽  
Empirical Calibration ◽  
Paris Area ◽  
The Difference

Abstract. CO2 emission estimates from urban areas can be obtained with a network of in-situ instruments measuring atmospheric CO2 combined with high-resolution (inverse) transport modeling. The distribution of CO2 emissions being highly heterogeneous in space and variable in time in urban areas, gradients of atmospheric CO2 need to be measured by numerous instruments placed at multiple locations around and possibly within these urban areas, which calls for the development of lower-cost medium precision sensors to allow a deployment at required densities. Medium precision is here set to be a random error (uncertainty) on hourly measurements of ±1 ppm or less, a precision requirement based on previous studies of network design in urban areas. Here we present tests of a HPP commercial NDIR sensors manufactured by Senseair AB performed in the laboratory and at actual field stations, the latter for CO2 concentration in the Paris area. The lower-cost medium precision sensors are shown to be sensitive to atmospheric pressure and temperature conditions. The sensors respond linearly to CO2 when measuring calibration tanks, but the regression slope between measured and true CO2 differs between individual sensors and changes with time. In addition to pressure and temperature variations, humidity impacts the measurement of CO2, all causing systematic errors. In the field, an empirical calibration strategy is proposed based on parallel measurements with the lower-cost medium precision sensors and a high-precision instrument cavity ring-down instrument during 6 month. This empirical calibration method consists of using a multiple regression approach to create a model of the errors defined as the difference of CO2 measured by the lower-cost medium precision sensors relative to a calibrated high-precision instrument, based on predictors of air temperature, pressure and humidity. This error model shows good performances to explain the observed drifts of the lower-cost medium precision sensors on time scales of up to 1–2 months when trained against 1–2 weeks of high-precision instrument time series. Residual errors are contained within the ±1 ppm target, showing the feasibility to use networks of HPP instruments for urban CO2 networks, provided that they could be regularly calibrated against one anchor reference high-precision instrument.

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