Narayan Kanishka B, Di Vittorio Alan V, Margiotta Evan, Spawn-Lee Seth, Gibbs Holly K
Joint Global Change Research Institute (JGCRI), Pacific Northwest National Lab (PNNL), Washington, DC, USA.
Lawrence Berkeley National Lab (LBNL), Berkeley, CA, USA.
Sci Data. 2025 Apr 16;12(1):638. doi: 10.1038/s41597-025-04723-4.
Soil and vegetation carbon stocks play a critical role in human-Earth system models. These stocks (denominated as densities in MgC/ha) affect variables such as land use change emissions and also influence land use change pathways under climate forcing scenarios where terrestrial carbon is assigned a carbon price. Here we present reharmonized soil and vegetation carbon densities both at the 5-arcmin resolution grid cell level and also aggregated to 235 water sheds for 4 land use types (Cropland, Grazed land, Urban land and unmanaged vegetation) and 15 unmanaged land cover types. Moreover, we use the distribution of carbon within and across pixels to define statistical "states" of carbon, once again differentiated by land type. These statistical states are used to define a range of possible carbon values that can be used for defining initial conditions of soil and vegetation carbon in human-Earth system models. We implement these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses, especially when terrestrial carbon is assigned a carbon price.
土壤和植被碳储量在人类-地球系统模型中起着关键作用。这些储量(以MgC/ha的密度表示)会影响诸如土地利用变化排放等变量,并且在为陆地碳设定碳价格的气候强迫情景下,还会影响土地利用变化路径。在此,我们展示了重新协调后的土壤和植被碳密度,其在5弧分分辨率的网格单元层面上,并且还汇总为235个流域的4种土地利用类型(农田、放牧地、城市用地和未管理植被)以及15种未管理土地覆盖类型。此外,我们利用像素内和像素间的碳分布来定义碳的统计“状态”,再次按土地类型进行区分。这些统计状态用于定义一系列可能的碳值,可用于确定人类-地球系统模型中土壤和植被碳的初始条件。我们将这些数据应用于一个先进的多部门动态模型,即全球变化分析模型(GCAM),并表明这些新数据改善了几种土地利用响应,特别是在为陆地碳设定碳价格的情况下。