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Methane (CH4) is a potent greenhouse gas and a key precursor of tropospheric ozone, itself a powerful greenhouse gas and air pollutant. Methane emissions across Queensland's Surat Basin, Australia, result from a mix of activities, including the production and processing of coal seam gas (CSG). We measured methane concentrations over 1.5 years from two monitoring stations established 80 km apart on either side of the main CSG belt located within a study area of 350 km × 350 km. Using an inverse modelling approach coupled with a bottom-up inventory, we quantify methane emissions from this area. The inventory suggests that the total emission is 173.2 × 106 kg CH4 yr−1, with grazing cattle contributing about half of that, cattle feedlots ∼ 25 %, and CSG processing ∼ 8 %. Using the inventory emissions in a forward regional transport model indicates that the above sources are significant contributors to methane at both monitors. However, the model underestimates approximately the highest 15 % of the observed methane concentrations, suggesting underestimated or missing emissions. An efficient regional Bayesian inverse model is developed, incorporating an hourly source–receptor relationship based on a backward-in-time configuration of the forward regional transport model, a posterior sampling scheme, and the hourly methane observations and a derived methane background. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical to the gridded bottom-up inventory emissions across the domain with an uncertainty of 3 % of the averages best describes the observed methane. Having only two stations is not adequate at sampling distant source areas of the study domain, and this necessitates a small prior uncertainty. This inverse setup yields a total emission of (165.8 ± 8.5) × 106 kg CH4 yr−1, slightly smaller than the inventory total. However, in a subdomain covering the CSG development areas, the inferred emissions are (63.6 ± 4.7) × 106 kg CH4 yr−1, 33 % larger than those from the inventory. We also infer seasonal variation of methane emissions and examine its correlation with climatological rainfall in the area.

Ice core records of the major atmospheric greenhouse gases (CO2, CH4, N2O) and their isotopologues covering recent centuries provide evidence of biogeochemical variations during the Late Holocene and pre-industrial periods and over the transition to the industrial period. These records come from a number of ice core and firn air sites and have been measured in several laboratories around the world and show common features but also unresolved differences. Here we present revised records, including new measurements, performed at the CSIRO Ice Core Extraction LABoratory (ICELAB) on air samples from ice obtained at the high-accumulation site of Law Dome (East Antarctica). We are motivated by the increasing use of the records by the scientific community and by recent data-handling developments at CSIRO ICELAB. A number of cores and firn air samples have been collected at Law Dome to provide high-resolution records overlapping recent, direct atmospheric observations. The records have been updated through a dynamic link to the calibration scales used in the Global Atmospheric Sampling LABoratory (GASLAB) at CSIRO, which are periodically revised with information from the latest calibration experiments. The gas-age scales have been revised based on new ice-age scales and the information derived from a new version of the CSIRO firn diffusion model. Additionally, the records have been revised with new, rule-based selection criteria and updated corrections for biases associated with the extraction procedure and the effects of gravity and diffusion in the firn. All measurements carried out in ICELAB–GASLAB over the last 25 years are now managed through a database (the ICElab dataBASE or ICEBASE), which provides consistent data management, automatic corrections and selection of measurements, and a web-based user interface for data extraction. We present the new records, discuss their strengths and limitations, and summarise their main features. The records reveal changes in the carbon cycle and atmospheric chemistry over the last 2 millennia, including the major changes of the anthropogenic era and the smaller, mainly natural variations beforehand. They provide the historical data to calibrate and test the next inter-comparison of models used to predict future climate change (Coupled Model Inter-comparison Project – phase 6, CMIP6). The datasets described in this paper, including spline fits, are available at https://doi.org/10.25919/5bfe29ff807fb (Rubino et al., 2019).

Atmospheric CO2 perturbations from simulated leaks have been used to determine the minimum statistically significant emissions that can be detected above background concentrations using a single atmospheric station. The study uses high precision CO2 measurements from the Arcturus atmospheric monitoring station in the Bowen Basin, Australia. A statistical model of the observed CO2 signal was constructed, combining both a regression and a time series model. A non-parametric goodness of fit approach using the Kolmogorov-Smirnoff (KS) test was then used to test whether simulated perturbations can be detected against the modelled expected value of the background for certain hours of the day and for particular seasons. The KS test calculates the probability that the modelled leak perturbation could be caused by natural variation in the background. Using pre-whitened data and selecting optimum test conditions, minimum detectable leaks located 1 km from the measurement station were estimated at 22 tpd for an area source of size 100 m x 100 m and 14 tpd for a point source at a KS cutoff defined by using the formal p-value of 0.05. These are very large leaks located only 1 km from the station and have a high false alarm rate of 56%. An alternative p-value could be chosen to reduce the false alarm rate but then the minimum detectable leaks are larger. A long term, single measurement station monitoring program that is unconstrained by prior information on the possible direction or magnitude of a leak, and based solely on detection of perturbations of CO2 due to leakage above a (naturally noisy) background signal, is likely to take one or more years to detect leaks of the order of 10 kt p.a. The sensitivity of detection of a leak above a background signal could be greatly improved through the installation of additional atmospheric monitoring stations or through greater prior knowledge about the location and size of a suspected leak.