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首页> 外文期刊>Journal of Climate >Quantifying environmental drivers of future tropical forest extent.
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Quantifying environmental drivers of future tropical forest extent.

机译:量化未来热带森林范围的环境驱动因素。

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Future changes in atmospheric greenhouse gas concentrations, and their associated influences on climate, will affect the future sustainability of tropical forests. While dynamic global vegetation models (DGVMs) represent the processes by which climate and vegetation interact, there is limited quantitative understanding of how specific environmental drivers each affect the simulated patterns of vegetation behavior and the resultant tropical forest fraction. Here, an attempt is made to improve on the qualitative understanding of how changes in dry season length, temperature, and CO2 combine to drive forest changes. Investigation of these topics is undertaken by integrating the Hadley Centre Climate Model version 3, run at lower spatial resolution with a coupled climate-carbon cycle (HadCM3LC), to steady state. This represents the situation where vegetation has adjusted fully to the prevailing climate and vice versa, permitting direct analysis of how climate and vegetation interact. These links are quantified by fitting the simulated tropical broadleaf tree fraction with a simple function of CO2 concentration, surface temperature, and dry season length. The resulting empirical function (denoted dry season resilience or DSR) is able to predict a sustainable tropical broadleaf fraction in this model across a very wide range of climates. The DSR function can also be used to compare the importance of different environmental drivers and to explore other emissions scenarios. While this DSR function is specific to the vegetation-land surface scheme in HadCM3LC, the method employed in this work is applicable to steady-state simulations from other vegetation-land surface schemes. The DSR metric is applied first as a framework to evaluate the DGVM by comparison of the simulated and observed forest fractions. For tropical broadleaf resilience in this model, a warming of 1 degrees C is approximately equivalent to a 2-week increase in dry season. In HadCM3LC climate model projections under the International Panel on Climate Change's (IPCC's) Special Report on Emissions Scenarios (SRES) A1B scenario, twenty-first-century increases in forest resilience due to CO2 fertilization approximately balance the tropical mean decrease from warming (the relative importance of rainfall and temperature changes depends on the uncertain spatial pattern of rainfall change). DSR is a tool that could be applied to different vegetation models to help us understand and narrow uncertainty in tropical forest projections.
机译:大气中温室气体浓度的未来变化及其对气候的影响,将影响热带森林的未来可持续性。尽管动态的全球植被模型(DGVM)代表了气候和植被相互作用的过程,但是对于特定的环境驱动因素如何影响模拟的植被行为模式和由此产生的热带森林组成部分的定量认识有限。在此,我们尝试在定性上提高对旱季长度,温度和CO 2 的变化如何组合以驱动森林变化的理解。通过将在较低空间分辨率下运行的哈德利中心气候模型版本3与气候碳循环耦合(HadCM3LC)集成到稳态,来进行这些主题的研究。这代表了植被已完全适应当前气候的情况,反之亦然,从而可以直接分析气候与植被之间的相互作用。通过将模拟的热带阔叶树分数与CO 2 浓度,地表温度和干旱季节长度的简单函数拟合,可以量化这些联系。由此产生的经验函数(表示为干旱季节适应力或DSR)能够在非常广泛的气候范围内预测该模型中可持续的热带阔叶叶面积。 DSR功能还可用于比较不同环境驱动因素的重要性并探索其他排放情景。尽管此DSR功能特定于HadCM3LC中的植被-土地表面方案,但该工作中采用的方法适用于其他植被-土地表面方案的稳态模拟。首先通过比较模拟和观察到的森林部分,将DSR指标用作评估DGVM的框架。对于此模型中的热带阔叶韧性,升温1摄氏度大约相当于干旱季节增加2周。在国际气候变化专门委员会(IPCC)的排放情景特别报告(SRES)A1B情景下的HadCM3LC气候模型预测中,由于CO 2 施肥导致的21世纪森林复原力增加大致平衡了气候变暖导致的热带平均值下降(降雨和温度变化的相对重要性取决于降雨变化的不​​确定空间格局)。 DSR是一种可以应用于不同植被模型的工具,可以帮助我们了解和缩小热带森林投影的不确定性。

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