Challenges in Scaling Up Greenhouse Gas Fluxes: Experience From the UK Greenhouse Gas Emissions and Feedbacks Program. Issue 5 (17th May 2022)

• Record Type: Journal Article
• Authors: Levy, Peter; Clement, Robert; Cowan, Nick; Keane, Ben; Myrgiotis, Vasilis; van Oijen, Marcel; Smallman, T. Luke; Toet, Sylvia; Williams, Mathew
• Is Part Of: Journal of geophysical research. Volume 127:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-17
• Subjects: Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2021JG006743
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: 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. Plain Language Summary: Greenhouse gases cause climate change, and we need to know how much is emitted each year across the globe. As well as coming from burning fossil fuels, plants and soil also take up and emit these gases, and we need to be able to quantify this in order to understand how best to tackle climate change. However, we can only measure these emissions over very small areas, at only a few locations, and for relatively short periods of time. Extrapolating from these measurements to a whole country introduces several uncertainties which are often largely ignored. Here, we examine progress in tackling this problem and focus on better statistical methods to properly identify and account for the errors that are introduced by the large change in scale. Another focus is the development of instrumentation that can measure the gas emissions over larger scales and run continuously. Earth observation from satellites provides a promising source of data for the future, but cannot yet provide direct measurements of gas emissions. The so‐called "Bayesian" approach to modeling, which allows us to relate observations to previous knowledge via the theory of conditional probability, provides us with a coherent method for combining data from different sources, accounting for their uncertainties, and propagating this through to the uncertainties associated with predictions of national‐scale fluxes. Key Points: We reviewed some of the challenges in accurately estimating fluxes of GHGs at a national scale Uncertainty arises from imperfectly‐known models, parameters, and inputs used in the extrapolation Bayesian principles allow us to quantify this whilst combining information sources in a coherent way