Collaborative Research and NEON: MSB Category 2: PalEON - a PaleoEcological Observatory Network to Assess Terrestrial Ecosystem Models

合作研究和 NEON：MSB 类别 2：PalEON - 评估陆地生态系统模型的古生态观测站网络

• 批准号: 1241930
• 负责人: Neil Pederson
• 金额: $ 44.19万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1241930&HistoricalAwards=false
• 关键词: Collaborative; Research; NEON; MSB; Category

Because of the slow pace of terrestrial ecosystem processes, including the slow generation time, growth rate, and decomposition rate of trees, the impact of changing climate and disturbance on forests plays out over hundreds of years. For this reason, terrestrial ecosystem models are used to anticipate the centennial scale projections of forest response to environmental change. Current terrestrial ecosystem model predictions vary widely and results have large statistical uncertainties. Furthermore, testing and calibration of these models relies on short term (sub-daily to decadal) data that fail to capture longer term trends and infrequent extreme events. The capacity of ecosystem models for scientific inference and long-term prediction would be greatly improved if uncertainties can be reduced through rigorous testing against observational data. PalEON is an interdisciplinary team of paleoecologists, statisticians, and modelers that have partnered to rigorously synthesize longer term paleoecological data and incorporate into ecosystem models to provide a deeper understanding of past dynamics and to use this knowledge to improve long-term forecasting capabilities.Funds are provided to address four objectives and associated research questions: 1) Validation: How well do ecosystem models simulate decadal-to-centennial dynamics when confronted with past climate change, and what limits model accuracy? 2) Initialization: How sensitive are ecosystem models to initialization state and equilibrium assumptions? Do data-constrained simulations of centennial-scale dynamics improve 20thcentury simulations? 3) Inference: Was the terrestrial biosphere a carbon sink or source during the Little Ice Age and Medieval Climate Anomaly? and 4) Improvement: How can parameters and processes responsible for data-model divergences be improved? The data synthesis will include wide range of ecosystems, encompasses past climate variations that were large enough to affect tree growth rates, disturbance regimes, and forest demography, and leverages available paleodata. The synthesis will include 1) fossil pollen and Public Land Survey data to reconstruct forest composition, 2) sedimentary charcoal, stand-age and firescar indicators of past disturbance regimes, 3) tree-ring records of tree growth rates, and 4) multiple paleoclimatic proxies and paleoclimatic simulations. Bayesian hierarchical statistical models will be used to reconstruct key ecological variables and their associated uncertainty estimates. A standardized model intercomparison involving 13 ecosystem modeling groups will be used to evaluate the robustness of the modeling approach.Three areas will be emphasized for PalEON's broader impacts. Community Building: The PalEON research community has doubled over the past 10 months, with more than 60 participants now. It is anticipated to nearly another doubling over the next five years, and the funds will allow the ongoing community-building via annual large meetings and task-oriented workshops. Interdisciplinary Training and Mentoring: A new generation of researchers will be trained to naturally conceptualize large spatial and temporal scales and to approach ecological forecasting as an integrative activity spanning data collection to model prediction. Eight postdocs and seven graduate students will be involved in proposed PalEON research, with multiple opportunities for cross-training. Additionally, the PalEON Summer Short Course provides an intensive cross-training experience for young scientists in all areas encompassed by PalEON. The 2012 course will be followed by courses in 2014 and 2016. Building Scientific Infrastructure: All PalEON datasets will be made publicly available upon publication, as will our new data-assimilation methods and model intercomparison protocols. Tools will be developed for optimal site selection (given the goal of reducing the integrated prediction uncertainty about past vegetation and climate over space and time) and will distribute a publicly available webtool version that will be linked directly to the Neotoma Paleoecology Database.

由于陆地生态系统过程缓慢，包括树木的生成时间、生长速度和分解速度缓慢，气候变化和干扰对森林的影响需要数百年的时间。因此，陆地生态系统模型用于预测森林对环境变化的响应的百年规模预测。目前的陆地生态系统模型预测差异很大，结果具有很大的统计不确定性。此外，这些模型的测试和校准依赖于短期（次日到十年）数据，无法捕捉长期趋势和罕见的极端事件。如果能够通过对观测数据的严格测试来减少不确定性，生态系统模型的科学推理和长期预测能力将大大提高。 PalEON 是一个由古生态学家、统计学家和建模师组成的跨学科团队，他们合作严格综合长期古生态数据并纳入生态系统模型，以更深入地了解过去的动态，并利用这些知识来提高长期预测能力。旨在解决四个目标和相关研究问题： 1）验证：在面对过去的气候变化时，生态系统模型模拟十年到百年动态的效果如何，以及是什么限制了模型的准确性？ 2）初始化：生态系统模型对初始化状态和均衡假设的敏感度如何？对百年尺度动力学的数据约束模拟是否可以改善 20 世纪的模拟？ 3）推论：小冰期和中世纪气候异常时期陆地生物圈是碳汇还是碳源？ 4）改进：如何改进导致数据模型差异的参数和流程？数据综合将包括广泛的生态系统，涵盖过去足以影响树木生长率、干扰状况和森林人口统计的气候变化，并利用现有的古数据。该综合将包括 1) 化石花粉和公共土地调查数据，以重建森林组成；2) 沉积木炭、过去扰动状况的林龄和火痕指标；3) 树木生长率的年轮记录；以及 4) 多种古气候代理和古气候模拟。贝叶斯分层统计模型将用于重建关键生态变量及其相关的不确定性估计。将使用涉及 13 个生态系统建模组的标准化模型比较来评估建模方法的稳健性。将强调三个领域以实现 PalEON 的更广泛影响。社区建设：PalEON 研究社区在过去 10 个月里翻了一番，目前有 60 多名参与者。预计未来五年将几乎再翻一番，这些资金将用于通过年度大型会议和面向任务的研讨会进行持续的社区建设。跨学科培训和指导：新一代研究人员将接受培训，自然地概念化大的空间和时间尺度，并将生态预测作为涵盖数据收集到模型预测的综合活动。八名博士后和七名研究生将参与拟议的 PalEON 研究，并有多种交叉培训的机会。此外，PalEON 夏季短期课程为 PalEON 所涵盖的所有领域的年轻科学家提供强化的交叉培训经验。 2012 年课程之后将是 2014 年和 2016 年的课程。 建设科学基础设施：所有 PalEON 数据集将在出版后公开，我们的新数据同化方法和模型相互比较协议也是如此。将开发用于最佳地点选择的工具（考虑到减少过去植被和气候在空间和时间上的综合预测不确定性的目标），并将分发一个公开可用的网络工具版本，该版本将直接链接到 Neotoma 古生态数据库。