INIA-CIFOR, Ctra. La Coruña km. 7.5, 28040, Madrid, Spain.
Aix-Marseille Université/CNRS/IRD, CEREGE, 13545, Aix-en-Provence, France.
Glob Chang Biol. 2017 Jul;23(7):2915-2927. doi: 10.1111/gcb.13597. Epub 2017 Feb 20.
Forest performance is challenged by climate change but higher atmospheric [CO ] (c ) could help trees mitigate the negative effect of enhanced water stress. Forest projections using data assimilation with mechanistic models are a valuable tool to assess forest performance. Firstly, we used dendrochronological data from 12 Mediterranean tree species (six conifers and six broadleaves) to calibrate a process-based vegetation model at 77 sites. Secondly, we conducted simulations of gross primary production (GPP) and radial growth using an ensemble of climate projections for the period 2010-2100 for the high-emission RCP8.5 and low-emission RCP2.6 scenarios. GPP and growth projections were simulated using climatic data from the two RCPs combined with (i) expected c ; (ii) constant c = 390 ppm, to test a purely climate-driven performance excluding compensation from carbon fertilization. The model accurately mimicked the growth trends since the 1950s when, despite increasing c , enhanced evaporative demands precluded a global net positive effect on growth. Modeled annual growth and GPP showed similar long-term trends. Under RCP2.6 (i.e., temperatures below +2 °C with respect to preindustrial values), the forests showed resistance to future climate (as expressed by non-negative trends in growth and GPP) except for some coniferous sites. Using exponentially growing c and climate as from RCP8.5, carbon fertilization overrode the negative effect of the highly constraining climatic conditions under that scenario. This effect was particularly evident above 500 ppm (which is already over +2 °C), which seems unrealistic and likely reflects model miss-performance at high c above the calibration range. Thus, forest projections under RCP8.5 preventing carbon fertilization displayed very negative forest performance at the regional scale. This suggests that most of western Mediterranean forests would successfully acclimate to the coldest climate change scenario but be vulnerable to a climate warmer than +2 °C unless the trees developed an exaggerated fertilization response to [CO ].
森林表现受到气候变化的挑战，但大气中较高的CO 2 可以帮助树木减轻增强的水分胁迫的负面影响。使用数据同化与机制模型进行的森林预测是评估森林表现的一种有价值的工具。首先，我们使用来自 12 种地中海树种（6 种针叶树和 6 种阔叶树）的树木年代学数据在 77 个地点校准了一个基于过程的植被模型。其次，我们使用高排放 RCP8.5 和低排放 RCP2.6 情景下 2010-2100 年期间的气候预测集合模拟了总初级生产力（GPP）和径向生长。使用来自这两个 RCP 的气候数据模拟 GPP 和生长预测，同时结合了 (i) 预期的[CO 2 ]；(ii) 常数[CO 2 ]=390ppm，以测试排除碳施肥补偿的纯粹气候驱动性能。尽管大气中[CO 2 ]不断增加，但增强的蒸发需求排除了对生长的全球净积极影响，因此该模型准确地模拟了自 20 世纪 50 年代以来的生长趋势。模型模拟的年生长和 GPP 表现出相似的长期趋势。在 RCP2.6 下（即相对于工业化前温度升高+2°C），森林表现出对未来气候的抵抗力（表现为生长和 GPP 的非负趋势），除了一些针叶树地点外。在 RCP8.5 下使用指数增长的[CO 2 ]和气候，碳施肥超过了该情景下高度受限的气候条件的负面影响。这种影响在 500ppm 以上（已经超过+2°C）时尤为明显，这似乎不现实，并且可能反映了模型在高[CO 2 ]校准范围之外的性能不佳。因此，在 RCP8.5 下防止碳施肥的森林预测显示，在区域尺度上，森林表现非常负面。这表明，大多数西地中海森林将成功适应最寒冷的气候变化情景，但除非树木对[CO 2 ]表现出夸张的施肥反应，否则它们将容易受到比+2°C 更暖的气候的影响。
