Department of Geography and Environmental Science, School of Archaeology, Geography and Environmental Science (SAGES), University of Reading, Whiteknights, Reading, UK.
Department of Meteorology, University of Reading, Whiteknights, Reading, UK.
Glob Chang Biol. 2022 Jan;28(1):201-226. doi: 10.1111/gcb.15929. Epub 2021 Oct 25.
There is a major concern for the fate of Amazonia over the coming century in the face of anthropogenic climate change. A key area of uncertainty is the scale of rainforest dieback to be expected under a future, drier climate. In this study, we use the middle Holocene (ca. 6000 years before present) as an approximate analogue for a drier future, given that palaeoclimate data show much of Amazonia was significantly drier than present at this time. Here, we use an ensemble of climate and vegetation models to explore the sensitivity of Amazonian biomes to mid-Holocene climate change. For this, we employ three dynamic vegetation models (JULES, IBIS, and SDGVM) forced by the bias-corrected mid-Holocene climate simulations from seven models that participated in the Palaeoclimate Modelling Intercomparison Project 3 (PMIP3). These model outputs are compared with a multi-proxy palaeoecological dataset to gain a better understanding of where in Amazonia we have most confidence in the mid-Holocene vegetation simulations. A robust feature of all simulations and palaeodata is that the central Amazonian rainforest biome is unaffected by mid-Holocene drought. Greater divergence in mid-Holocene simulations exists in ecotonal eastern and southern Amazonia. Vegetation models driven with climate models that simulate a drier mid-Holocene (100-150 mm per year decrease) better capture the observed (palaeodata) tropical forest dieback in these areas. Based on the relationship between simulated rainfall decrease and vegetation change, we find indications that in southern Amazonia the rate of tropical forest dieback was ~125,000 km per 100 mm rainfall decrease in the mid-Holocene. This provides a baseline sensitivity of tropical forests to drought for this region (without human-driven changes to greenhouse gases, fire, and deforestation). We highlight the need for more palaeoecological and palaeoclimate data across lowland Amazonia to constrain model responses.
面对人为气候变化,下个世纪人们对亚马逊命运的主要关注点是。一个关键的不确定性领域是在未来更干燥的气候下预计会出现多大规模的雨林衰退。在这项研究中,我们使用中全新世(大约在现在前 6000 年)作为未来更干燥的近似模拟,因为古气候数据表明,此时亚马逊地区的大部分地区明显比现在干燥。在这里,我们使用气候和植被模型的集合来探索亚马逊生物群落对中全新世气候变化的敏感性。为此,我们采用了三种动态植被模型(JULES、IBIS 和 SDGVM),这些模型由参与古气候建模比较计划 3(PMIP3)的七个模型的中全新世气候模拟的偏差校正数据驱动。将这些模型输出与多代理古生态学数据集进行比较,以更好地了解在亚马逊地区,我们对中全新世植被模拟的置信度最高的位置。所有模拟和古数据的一个可靠特征是,亚马逊中部雨林生物群不受中全新世干旱的影响。中全新世模拟的更大分歧存在于东部和南部的亚马逊生态过渡区。使用模拟中全新世较干燥气候的气候模型驱动的植被模型(每年减少 100-150 毫米)更好地捕捉到了这些地区观察到的(古数据)热带森林衰退。根据模拟降雨量减少与植被变化之间的关系,我们发现有迹象表明,在南部亚马逊地区,热带森林衰退的速度在中全新世每减少 100 毫米降雨量约为 125000 平方公里。这为该地区的热带森林对干旱的敏感性提供了一个基准(不考虑人为引起的温室气体、火灾和森林砍伐变化)。我们强调需要在亚马逊低地地区获得更多的古生态学和古气候数据,以限制模型的响应。