Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Programme
Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Programme
复制标题
扩大温室气体通量的挑战：英国温室气体排放和反馈计划的经验
DOI：
10.1002/essoar.10509113.1
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Levy P
中科院分区：
文献类型：
--
作者：
Levy P
温室气体在全球气候变化中的作用现已得到广泛认可，因此显然需要测量排放量并验证缓解措施的有效性。为此，需要对国家范围和长期的温室气体平衡作出可靠的估计，但这些估计很难准确作出。由于测量技术通常限于相对较小的空间和时间尺度，因此在将这些技术转化为区域尺度的长期估计数方面存在着一个根本问题。关键挑战在于对短期点观测进行空间和时间升级，以估计大规模的年度总量，并量化与这种升级相关的不确定性。在这里，我们回顾了一些解决这个问题的方法，并综合了最近的英国温室气体排放和反馈计划，旨在确定和解决这些挑战的工作。解决比例问题的办法包括：仪器的发展，这意味着可以产生具有更大空间覆盖范围的近连续数据集;利用数据中的空间信息解决外推到更大领域问题的地质统计方法;消除外推到更长时间尺度的不确定性的更严格的统计方法;估计模型汇总误差的分析方法; C通量测量误差的增强估计;以及遥感数据校准过程模型以生成概率性区域C通量估计的新用途。
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.