The long-term net sink of carbon (C), nitrogen (N) and greenhouse gases (GHGs) in the northern permafrost region is projected to weaken or shift under climate change. But large uncertainties remain, even on present-day GHG budgets. We compare bottom-up (data-driven upscaling, process-based models) and top-down budgets (atmospheric inversion models) of the main GHGs (CO2, CH4, and N2O) and lateral fluxes of C and N across the region over 2000-2020. Bottom-up approaches estimate higher land to atmosphere fluxes for all GHGs compared to top-down atmospheric inversions. Both bottom-up and top-down approaches respectively show a net sink of CO2 in natural ecosystems (-31 (-667, 559) and -587 (-862, -312), respectively) but sources of CH4 (38 (23, 53) and 15 (11, 18) Tg CH4-C yr-1) and N2O (0.6 (0.03, 1.2) and 0.09 (-0.19, 0.37) Tg N2O-N yr-1) in natural ecosystems. Assuming equal weight to bottom-up and top-down budgets and including anthropogenic emissions, the combined GHG budget is a source of 147 (-492, 759) Tg CO2-Ceq yr-1 (GWP100). A net CO2 sink in boreal forests and wetlands is offset by CO2 emissions from inland waters and CH4 emissions from wetlands and inland waters, with a smaller additional warming from N2O emissions. Priorities for future research include representation of inland waters in process-based models and compilation of process-model ensembles for CH4 and N2O. Discrepancies between bottom-up and top-down methods call for analyses of how prior flux ensembles impact inversion budgets, more in-situ flux observations and improved resolution in upscaling.

The role of greenhouse gases (GHGs) in global climate change is now well recognised and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long-term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short-term, point observations to estimate large-scale annual totals, and quantifying the uncertainty associated with this upscaling. Here, we review some approaches to this problem, and synthesise the work in the recent UK Greenhouse Gas Emissions and Feedbacks Programme, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near-continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterise the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates.