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As part of the CarbonWatch-NZ research programme, air samples were collected at 28 sites around Auckland, New Zealand, to determine the atmospheric ratio ( R CO ) of excess (local enhancement over background) carbon monoxide to fossil CO 2 (CO 2 ff). Sites were categorized into seven types (background, forest, industrial, suburban, urban, downwind and motorway) to observe R CO around Auckland. Motorway flasks observed R CO of 14 ± 1 ppb ppm -1 and were used to evaluate traffic R CO . The similarity between suburban (14 ± 1 ppb ppm -1 ) and traffic R CO suggests that traffic dominates suburban CO 2 ff emissions during daytime hours, the period of flask collection. The lower urban R CO (11 ± 1 ppb ppm -1 ) suggests that urban CO 2 ff emissions are comprised of more than just traffic, with contributions from residential, commercial and industrial sources, all with a lower R CO than traffic. Finally, the downwind sites were believed to best represent R CO for Auckland City overall (11 ± 1 ppb ppm -1 ). We demonstrate that the initial discrepancy between the downwind R CO and Auckland's estimated daytime inventory R CO (15 ppb ppm -1 ) can be attributed to an overestimation in inventory traffic CO emissions. After revision based on our observed motorway R CO , the revised inventory R CO (12 ppb ppm -1 ) is consistent with our observations. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.

Government policies currently commit us to surface warming of three to four degrees Celsius above pre-industrial levels by 2100, which will lead to enhanced ice-sheet melt. Ice-sheet discharge was not explicitly included in Coupled Model Intercomparison Project phase 5, so effects on climate from this melt are not currently captured in the simulations most commonly used to inform governmental policy. Here we show, using simulations of the Greenland and Antarctic ice sheets constrained by satellite-based measurements of recent changes in ice mass, that increasing meltwater from Greenland will lead to substantial slowing of the Atlantic overturning circulation, and that meltwater from Antarctica will trap warm water below the sea surface, creating a positive feedback that increases Antarctic ice loss. In our simulations, future ice-sheet melt enhances global temperature variability and contributes up to 25 centimetres to sea level by 2100. However, uncertainties in the way in which future changes in ice dynamics are modelled remain, underlining the need for continued observations and comprehensive multi-model assessments.

Independent estimates of fossil fuel CO2 (CO2ff) emissions are key to ensuring that emission reductions and regulations are effective and provide needed transparency and trust. Point source emissions are a key target because a small number of power plants represent a large portion of total global emissions. Currently, emission rates are known only from self-reported data. Atmospheric observations have the potential to meet the need for independent evaluation, but useful results from this method have been elusive, due to challenges in distinguishing CO2ff emissions from the large and varying CO2 background and in relating atmospheric observations to emission flux rates with high accuracy. Here we use time-integrated observations of the radiocarbon content of CO2 ((14)CO2) to quantify the recently added CO2ff mole fraction at surface sites surrounding a point source. We demonstrate that both fast-growing plant material (grass) and CO2 collected by absorption into sodium hydroxide solution provide excellent time-integrated records of atmospheric (14)CO2 These time-integrated samples allow us to evaluate emissions over a period of days to weeks with only a modest number of measurements. Applying the same time integration in an atmospheric transport model eliminates the need to resolve highly variable short-term turbulence. Together these techniques allow us to independently evaluate point source CO2ff emission rates from atmospheric observations with uncertainties of better than 10%. This uncertainty represents an improvement by a factor of 2 over current bottom-up inventory estimates and previous atmospheric observation estimates and allows reliable independent evaluation of emissions., Competing Interests: The authors declare no conflict of interest.

Predictive understanding of precipitation δ(2)H and δ(18)O in New Zealand faces unique challenges, including high spatial variability in precipitation amounts, alternation between subtropical and sub-Antarctic precipitation sources, and a compressed latitudinal range of 34 to 47 °S. To map the precipitation isotope ratios across New Zealand, three years of integrated monthly precipitation samples were acquired from >50 stations. Conventional mean-annual precipitation δ(2)H and δ(18)O maps were produced by regressions using geographic and annual climate variables. Incomplete data and short-term variation in climate and precipitation sources limited the utility of this approach. We overcome these difficulties by calculating precipitation-weighted monthly climate parameters using national 5-km-gridded daily climate data. This data plus geographic variables were regressed to predict δ(2)H, δ(18)O, and d-excess at all sites. The procedure yields statistically-valid predictions of the isotope composition of precipitation (long-term average root mean square error (RMSE) for δ(18)O = 0.6 ‰; δ(2)H = 5.5 ‰); and monthly RMSE δ(18)O = 1.9 ‰, δ(2)H = 16 ‰. This approach has substantial benefits for studies that require the isotope composition of precipitation during specific time intervals, and may be further improved by comparison to daily and event-based precipitation samples as well as the use of back-trajectory calculations.