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Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Programme
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  • Peter Levy,
  • Robert Jon Clement,
  • Nicholas Jon Cowan,
  • Ben Keane,
  • Vasileios Myrgiotis,
  • Marcel van Oijen,
  • Thomas Luke Smallman,
  • Sylvia Toet,
  • Mathew Williams
Peter Levy
Centre for Ecology and Hydrology

Corresponding Author:[email protected]

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Robert Jon Clement
University of Edinburgh
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Nicholas Jon Cowan
Centre for Ecology & Hydrology
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Ben Keane
University of York
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Vasileios Myrgiotis
University of Edinburgh
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Marcel van Oijen
Centre Ecology & Hydrology, Penicuik, Scotland
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Thomas Luke Smallman
University of Edinburgh
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Sylvia Toet
University of York
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Mathew Williams
University of Edinburgh
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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.