GHG budgets – atmosphere

This document is about quantification of GHG budgets for selected regions in the Arctic based on the assimilation of multi-disciplinary data layers into an atmospheric modelling framework, including the identification of key processes across disciplines that govern Arctic greenhouse gas cycles and their links to climate change, describes the work conducted within the atmospheric component of INTAROS Task 6.5. The main contributions for the work reported herein were produced by partner MPG, with additional contributions from UB.

The East Siberian Arctic Shelf hosts vast carbon reservoirs at risk of degradation and may be a strong emitter of methane to the atmosphere. Yet, estimates of its annual methane emissions and their key controls are highly uncertain. In the presented project, we estimated these emissions with a geostatistical inverse model from atmospheric observations over seventeen months in Tiksi (Russia), Barrow (Alaska) and Ambarchik (Russia). Our simulations yielded annual methane emissions of 0.3 – 1.5 Tg CH4, which is on the low end of previously reported estimates (0 – 17 Tg CH4 yr-1). Our geostatistical approach allows us to test the compatibility of a large number of spatiotemporal emissions patterns with the atmospheric signals. In this context, we specifically tested the suitability of novel data products from the INTAROS database to improve model performance. Our model attributes highest emissions to shallow waters and to ice-free and potentially freeze-up periods, but also finds substantial emissions during the ice-covered period. We do not detect substantial emissions of stored methane during ice breakup. Our results suggest that mixing and stratification of the water column and cracks in sea ice could be among the dominant controls of methane emissions from the shelf to the atmosphere. Other explanations are possible and discussed, including limitations of our study. The information provided by the INTAROS database led to minor improvements in the explained variability of atmospheric greenhouse gas time series, indicating the high quality of the novel products. However, since parameter selection basically replaced existing oceanic variables by better-performing new ones for the same parameter but did not add a previously omitted parameter to the highest-ranking models, we could not gain novel process insights. Our study suggests that the relevance of the shelf for the global atmospheric methane burden is currently small, but also reveals limitations of the Arctic atmospheric greenhouse gas observation network.

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