Bombi Pierluigi, D'Andrea Ettore, Rezaie Negar, Cammarano Mario, Matteucci Giorgio
Institute of Agro-environmental and Forest Biology, National Research Council of Italy (CNR-IBAF), Monterotondo, Italy.
LifeWatch-ITA, Mediterranean Thematic Center, National Research Council of Italy, Rome, Italy.
PLoS One. 2017 Dec 18;12(12):e0189468. doi: 10.1371/journal.pone.0189468. eCollection 2017.
Current expectations on future climate derive from coordinated experiments, which compile many climate models for sampling the entire uncertainty related to emission scenarios, initial conditions, and modelling process. Quantifying this uncertainty is important for taking decisions that are robust under a wide range of possible future conditions. Nevertheless, if uncertainty is too large, it can prevent from planning specific and effective measures. For this reason, reducing the spectrum of the possible scenarios to a small number of one or a few models that actually represent the climate pathway influencing natural ecosystems would substantially increase our planning capacity. Here we adopt a multidisciplinary approach based on the comparison of observed and expected spatial patterns of response to climate change in order to identify which specific models, among those included in the CMIP5, catch the real climate variation driving the response of natural ecosystems. We used dendrochronological analyses for determining the geographic pattern of recent growth trends for three European species of trees. At the same time, we modelled the climatic niche for the same species and forecasted the suitability variation expected across Europe under each different GCM. Finally, we estimated how well each GCM explains the real response of ecosystems, by comparing the expected variation with the observed growth trends. Doing this, we identified four climatic models that are coherent with the observed trends. These models are close to the highest range limit of the climatic variations expected by the ensemble of the CMIP5 models, suggesting that current predictions of climate change impacts on ecosystems could be underestimated.
当前对未来气候的预期源自协调实验,这些实验汇集了许多气候模型,用于对与排放情景、初始条件及建模过程相关的整个不确定性进行抽样。量化这种不确定性对于做出在广泛的可能未来条件下都稳健的决策很重要。然而,如果不确定性过大,就会妨碍制定具体有效的措施。因此,将可能情景的范围缩小到少数几个真正代表影响自然生态系统的气候路径的模型,将大幅提高我们的规划能力。在此,我们采用一种多学科方法,基于对气候变化响应的观测和预期空间模式的比较,以确定在CMIP5所包含的模型中,哪些特定模型捕捉到了驱动自然生态系统响应的真实气候变化。我们利用树木年代学分析来确定三种欧洲树种近期生长趋势的地理模式。同时,我们对相同树种的气候生态位进行建模,并预测在每个不同的全球气候模型(GCM)下整个欧洲预期的适宜性变化。最后,通过将预期变化与观测到的生长趋势进行比较,我们估计了每个GCM对生态系统真实响应的解释程度。通过这样做,我们确定了四个与观测趋势一致的气候模型。这些模型接近CMIP5模型集合预期的气候变化最高范围极限,这表明当前对气候变化对生态系统影响的预测可能被低估了。