Modeling the Vegetation of the Past

该项目利用古生态数据重建北美和欧洲过去的植被，测试了一个动态全球植被模型（DGVM）。DGVM由全耦合环流模式（GCM）模拟的过去气候驱动，古植被重建来自北美和欧洲的大陆尺度花粉和植物大化石数据数据库。过去的植被也可以作为过去气候变化的直接代表，最近的研究表明，DGVM可以用来帮助解释来自花粉和大化石数据的气候信号。这些技术可以评估非气候因素（如二氧化碳）在驱动植被变化中的作用。该项目能够识别和减少冰川时期气候重建中的偏差，因为这些因素与现在有很大不同。dgvm是用来了解在不同情景下陆地生态系统如何受到未来气候变化影响的主要工具之一。通过研究过去的时期，这些模型在模拟不同于当前条件下的生态系统变化方面的预测能力可以得到检验。模型运行的结果也可用于调查陆地生态系统所提供服务的过去变化，包括碳固存和水资源。这项工作还将对用于模拟过去气候的GCM进行评估，这将有助于了解其模拟未来全球变化的能力。该项目的结果将包括过去21000年环境条件的一致数据集。这将在项目结束时公开，并将为考古研究和陆地表面历史和过程的研究提供一个背景。这些结果将转化为可公开访问的在线教学资源。

This project tests a Dynamic Global Vegetation Model (DGVM) using reconstructions of past vegetation for North America and Europe based on paleoecological data. The DGVM is driven by past climate simulated with a fully coupled General Circulation Model (GCM), and the paleovegetation reconstructions are taken from continental scale databases of pollen and plant macrofossil data covering North America and Europe. Past vegetation can also serve as a direct proxy of past climate variations, and recent studies have shown that a DGVM can be used to help interpret the climate signal from pollen and macrofossil data. These techniques allow assessment of the role of non-climatic factors (e.g., CO2) in driving vegetation change. The project is able to identify and reduce bias in climate reconstructions for glacial periods, where these factors are significantly different from the present day.DGVMs are one of the primary tools used to understand how terrestrial ecosystems might be affected by future climate change under various scenarios. By studying past periods, the predictive ability of these models in simulating ecosystem change under conditions that are different from the present day can be tested. The results from the model runs can also be used to investigate past changes in services provided by terrestrial ecosystems, including carbon sequestration and water resources. The work will also provide an assessment of the GCM used to simulate past climates, which will help to understand its skill in simulating future global change. The project results will include a consistent dataset of environmental conditions covering the last 21000 years. This will be made publicly available at the end of the project, and will provide a context for archeological studies and studies of land-surface history and processes. These results will be transformed into publicly accessible online teaching resources.