Collaborative Research: A Late-Glacial Model System for Studying Fine-Scale Vegetational Responses to Abrupt Climate Change

由于温室气体浓度的上升，气候模型预测了未来的气候不同于今天地球上的任何地方，换句话说，它们没有现代的模拟。这样的预测极大地扰乱了预测植被反应的努力，因为大多数气候-植被模型依赖于模拟气候。美国中西部的晚冰期，大约在15,000到11,500年前，也是一个气候和植被非类比的时期。与今天相比，具有不同寻常的物种关联性的植物群落占据了主导地位。这些“非模拟”社区提供了一个理想的机会来研究在现代范围之外的条件下对快速气候变化的精细反应，并测试旨在预测植被对气候变化的反应的模型。该项目将采用多重代理方法进行环境重建，包括用于植被重建的化石花粉，用于阐明气候驱动因素的植被建模，以及作为对水分平衡的独立检查的水文重建。这项研究将建立中西部上冰期生态系统变化与北大西洋地区的时间相关性，这是一个长期争论的主题。它解决了古生态学中的突出问题之一--非相似化石组合。所提出的建模技术旨在预测对现代包络之外的气候的生物反应，这些气候很可能是温室气体变暖造成的，预测建模对此至关重要。

Because of rising concentrations of greenhouse gases, climate models predict future climates that are different from anywhere on Earth today, in other words, they have no modern analog. Such predictions greatly confound efforts to predict vegetation response because most climate-vegetation models rely on analog climates. The late-glacial period in the Midwestern United states, approximately 15,000 to 11,500 years ago, was also a time of non-analog climate and vegetation. Relative to today, plant communities with unusual associations of species dominated the landscape. These "non-analog" communities present an ideal opportunity to investigate fine-scale response to rapid climate change under conditions outside the modern envelope and to test models designed to predict vegetation responses to climate change. This project will employ a multiproxy approach to environmental reconstruction, involving fossil pollen for vegetation reconstruction, vegetation modeling to elucidate climatic drivers, and hydrologic reconstruction as an independent check on moisture balance.This study will establish the temporal correlation of late-glacial ecosystem change in the upper Midwest with the North Atlantic region, a subject of long debate. It tackles one of the outstanding problems in paleoecology, non-analog fossil assemblages. The modeling techniques proposed are designed to predict biotic response to climates outside the modern envelope, climates that greenhouse warming may well produce and for which predictive modeling is crucial.