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Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterisation

机译:簿记模型参数化土地利用变化通量不确定性的比较

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Fluxes from deforestation, changes in land cover, land use and management practices ( F LUC ?for simplicity) contributed to approximately 14?% of anthropogenic CO 2 emissions in?2009–2018. Estimating? F LUC accurately in space and in time remains, however, challenging, due to multiple sources of uncertainty in the calculation of these fluxes. This uncertainty, in turn, is propagated to global and regional carbon budget estimates, hindering the compilation of a consistent carbon budget and preventing us from constraining other terms, such as the natural land sink. Uncertainties in? F LUC estimates arise from many different sources, including differences in model structure (e.g. process based vs.?bookkeeping) and model parameterisation. Quantifying the uncertainties from each source requires controlled simulations to separate their effects. Here, we analyse differences between the two bookkeeping models used regularly in the global carbon budget estimates since?2017: the model by Hansis et al.?(2015)?(BLUE) and that by Houghton and Nassikas?(2017)?(HN2017). The two models have a very similar structure and philosophy, but differ significantly both with respect to F LUC ?intensity and spatiotemporal variability. This is due to differences in the land-use forcing but also in the model parameterisation. We find that the larger emissions in BLUE compared to HN2017 are largely due to differences in C?densities between natural and managed vegetation or primary and secondary vegetation, and higher allocation of cleared and harvested material to fast turnover pools in BLUE than in HN2017. Besides parameterisation and the use of different forcing, other model assumptions cause differences: in particular that BLUE represents gross transitions which leads to overall higher carbon losses that are also more quickly realised than HN2017.
机译:来自森林砍伐的助核,陆地覆盖,土地利用和管理实践(f luc?为简单起见)的变化导致约14?%的人为二氧化碳排放量2009-2018。估计?然而,由于在计算这些通量的计算中的多种不确定性来源,因此在空间和时间内准确地挑战。反过来,这种不确定性又传播到全球和区域碳预算估计,阻碍了一致的碳预算的汇编,并阻止我们限制了自然陆汇的其他条款。不确定性? F Luc估计来自许多不同的来源,包括模型结构的差异(例如,基于过程的VS.Bookkeeping)和模型参数化。量化每个来源的不确定性需要受控模拟来分离它们的效果。在这里,我们分析了自全球碳预算估算中定期使用的两种簿记模型之间的差异:2017年:Hansis等人的模型:(2015) ?(Blue),由Houghton和Nassikas?(2017)?(HN2017 )。这两种型号具有非常相似的结构和哲学,但对于F Luc的强度和时空变异性显着差异。这是由于土地使用迫使的差异,而且在模型参数化中。我们发现与HN2017相比的蓝色的较大排放主要是由于C?天然和管理植被或初级和次级植被之间的密度,以及将清除和收获材料的较高分配到蓝色的快速营业额而不是HN2017。除了参数化和使用不同的迫使,其他模型假设会导致差异:特别是蓝色代表总转变,这导致总体较高的碳损失比HN2017更快地实现。

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