US Department of Agriculture, Forest Service, Northern Research Station, St. Paul, Minnesota, United States of America.
PLoS One. 2013 Sep 10;8(9):e73222. doi: 10.1371/journal.pone.0073222. eCollection 2013.
Among terrestrial environments, forests are not only the largest long-term sink of atmospheric carbon (C), but are also susceptible to global change themselves, with potential consequences including alterations of C cycles and potential C emission. To inform global change risk assessment of forest C across large spatial/temporal scales, this study constructed and evaluated a basic risk framework which combined the magnitude of C stocks and their associated probability of stock change in the context of global change across the US. For the purposes of this analysis, forest C was divided into five pools, two live (aboveground and belowground biomass) and three dead (dead wood, soil organic matter, and forest floor) with a risk framework parameterized using the US's national greenhouse gas inventory and associated forest inventory data across current and projected future Köppen-Geiger climate zones (A1F1 scenario). Results suggest that an initial forest C risk matrix may be constructed to focus attention on short- and long-term risks to forest C stocks (as opposed to implementation in decision making) using inventory-based estimates of total stocks and associated estimates of variability (i.e., coefficient of variation) among climate zones. The empirical parameterization of such a risk matrix highlighted numerous knowledge gaps: 1) robust measures of the likelihood of forest C stock change under climate change scenarios, 2) projections of forest C stocks given unforeseen socioeconomic conditions (i.e., land-use change), and 3) appropriate social responses to global change events for which there is no contemporary climate/disturbance analog (e.g., severe droughts in the Lake States). Coupling these current technical/social limits of developing a risk matrix to the biological processes of forest ecosystems (i.e., disturbance events and interaction among diverse forest C pools, potential positive feedbacks, and forest resiliency/recovery) suggests an operational forest C risk matrix remains elusive.
在陆地环境中，森林不仅是大气碳（C）的最大长期汇，而且本身也容易受到全球变化的影响，潜在的后果包括 C 循环的改变和潜在的 C 排放。为了在大的时空尺度上为森林 C 的全球变化风险评估提供信息，本研究构建并评估了一个基本的风险框架，该框架结合了美国范围内全球变化背景下 C 储量的大小及其储量变化的可能性，对其进行了评估。在本分析中，森林 C 分为五个库，两个活库（地上和地下生物量）和三个死库（枯木、土壤有机质和林地凋落物），风险框架使用美国国家温室气体清单和相关的森林清查数据在当前和预计未来的柯本-戈尔气候带（A1F1 情景）中进行参数化。结果表明，可以构建一个初始森林 C 风险矩阵，使用基于清查的总储量和气候带之间变异性（即变异系数）的估计值，将注意力集中在森林 C 储量的短期和长期风险上（而不是在决策中实施）。这种风险矩阵的经验参数化突出了许多知识空白：1）在气候变化情景下森林 C 储量变化的可能性的可靠衡量标准，2）在无法预见的社会经济条件下（即土地利用变化）对森林 C 储量的预测，以及 3）对没有当代气候/干扰类似物的全球变化事件的适当社会反应（例如，在五大湖区的严重干旱）。将开发风险矩阵的当前技术/社会限制与森林生态系统的生物过程（即干扰事件以及不同森林 C 库之间的相互作用、潜在的正反馈和森林的弹性/恢复能力）相结合，表明操作森林 C 风险矩阵仍然难以捉摸。
