Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Program

Levy, Peter ORCID: https://orcid.org/0000-0002-8505-1901; Clement, Robert; Cowan, Nick ORCID: https://orcid.org/0000-0002-7473-7916; Keane, Ben; Myrgiotis, Vasilis; van Oijen, Marcel; Smallman, T. Luke; Toet, Sylvia; Williams, Mathew. 2022 Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Program. Journal of Geophysical Research: Biogeosciences, 127 (5), e2021JG006743. 22, pp. https://doi.org/10.1029/2021JG006743

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Abstract/Summary

The role of greenhouse gases (GHGs) in global climate change is now well recognized 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 the 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 quantify the uncertainty associated with this upscaling. Here, we review some approaches to this problem and synthesize the work in the recent UK Greenhouse Gas Emissions and Feedbacks Program, 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 characterize 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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1029/2021JG006743
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-) Unaffiliated
ISSN:	2169-8953
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Atmospheric Sciences
Date made live:	21 Jul 2022 09:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/532961

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