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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).

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 × 350 km2. Coupling bottom-up inventory and inverse modelling approaches, we quantify methane emissions from this area. The inventory suggests that the total emission is 173 × 106 kg CH4/yr, 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. The inferred emissions obtained from one of the inverse model setups that uses a Gaussian prior whose averages are identical 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 that is very similar to the total inventory emission. However, in a subdomain covering the CSG development areas, the inferred emissions are 33 % larger than those from the inventory.