Lakes at risk under climate change: Climate variability matters

该项目将加强对湖泊生态系统对全球变暖的响应能力的了解，特别是对气候和天气条件变化的响应能力。重点问题是，观测到的和预测的气候变暖的详细季节性模式以何种方式影响湖泊的物理和生物结构。这种方法的基本原理是，平均温度变化（多年或季节的平均值）不足以衡量生态系统所经历的变化，特别是因为同样的平均温度变化可以从亚日到年代际时间尺度的多种变暖模式中产生。因此，我们挑战了临界生态系统变化的风险随着平均温度的升高而准线性增加的假设。对于中欧及其他地区的一系列不同湖泊，该项目将系统地分析不同程度的当地和全球变暖对关键湖泊特性的影响，如分层事件和浮游生物种群动态。利用多年代际观测时间序列，已经观测到的气候变化将根据其平均年和季节变暖率进行分组，然后分析这些组内由于天气的短期变率而产生的生态变率。类似地，将通过使用统计和机制模型以及基于概率的分析框架，探讨明确涵盖季节天气演变的大范围可能实现的一系列未来情景下的生态影响和变化风险。预计该项目将大大有助于了解气候变化对生态系统时间变异性的影响。

The project will enhance the understanding of lake ecosystems’ responsiveness to global warming, particularly to the variability in climate and weather conditions. The focal question is in what ways the detailed seasonal pattern of observed and projected climate warming affects the physical and biological structure of lakes. The rationale of this approach is that mean temperature changes (averaged over years or seasons) are inadequate measures of the changes that ecosystems experience, especially since the same average temperature change can derive from a multitude of warming patterns from sub-daily to decadal temporal scales. We thus challenge the supposition that the risk of critical ecosystem change increases quasi-linearly with rising mean temperature. For a suite of different lakes in Central Europe and beyond, the project will systematically analyse effects of different degrees of local and global warming upon key lake properties such as stratification events and plankton population dynamics. Using multi-decadal observational time series, already observed climate changes will be grouped according to their average annual and seasonal warming rates, followed by analysis of the ecological variability within these groups as imposed by short-term variability in the weather. Analogously, ecological impacts and risks of change under a range of future scenarios that explicitly cover a large range of possible realisations of seasonal weather evolution will be explored by use of statistical and mechanistic models and a probability-based analytical framework. The project is expected to contribute substantially to the understanding of climate change impacts upon the temporal variability of ecosystems.