Quantifying the Amazon Isoprene Budget: Reconciling top-down versus bottom-up emission estimates

气候变化一词现在是一个家喻户晓的短语，我们习惯于听到温室气体水平上升和全球变暖。提高公众对环境问题认识的第一批事件之一是1980年代发现南极臭氧空洞。臭氧是一种气体，只占构成大气的所有气体的一小部分，但它在气候中非常重要。在高海拔地区（约15至30公里），有大量的臭氧，这对地球是件好事，因为它屏蔽了太阳有害的紫外线辐射。然而，臭氧是一种有毒物质，如果它在对流层（大气的最低部分）和表面积聚，那么这是不好的。对流层臭氧对我们有害，因为它是（a）温室气体，（B）影响人类呼吸系统和农作物产量的空气污染物。当被称为挥发性有机化合物（VOC）的物质从地表释放并随后在大气中反应时，臭氧在地表附近产生。VOCs可以从人类活动中排放，但它们主要是由生长在陆地上的植被排放的。在排放到大气中的所有生物源挥发性有机化合物中，没有一种比异戊二烯更重要，因为它能够迅速与其他化合物反应（产生对流层臭氧），而且排放量很大。异戊二烯也很重要，因为它是一种称为二次有机气溶胶（SOA）的非常小的颗粒的来源，这些颗粒会散射光，影响地球变暖的方式，也会对健康产生不利影响。我们需要知道（a）何时、（B）何处和（c）有多少异戊二烯排放到大气中，以便更好地了解对流层臭氧和SOA。目前，我们使用基于观测的通用计算机模型来模拟不同类型的植被（如树木或草）排放的异戊二烯量。亚马逊盆地拥有世界上最大的雨林，被认为是异戊二烯的最大来源之一，该盆地的异戊二烯排放量很难量化，因为很难测量这一基本上无法进入的偏远地区的排放量。对一种称为甲醛（HCHO）的气体的卫星观测包含了异戊二烯排放的信息，可用于确定陆地植被排放的异戊二烯量。我的提案的总体目标是利用卫星观测HCHO来准确量化亚马逊盆地的异戊二烯排放量。为了实现这一目标，我将为亚马逊开发一个新的独特的高分辨率模型，它将能够在比以前更精细的空间尺度上模拟异戊二烯排放和大气化学。然后，我将比较异戊二烯排放量从这个“自下而上”的模型与“自上而下”的异戊二烯排放量推断从卫星观测的HCHO，以确定区域或时间段，有显着的分歧之间的模型和观测，这突出了我们对异戊二烯排放量的理解不足。然后，我将开发一个改进的异戊二烯排放模型微调的“自下而上”的排放模型推断的“自上而下”的排放量，考虑到个别场景（利用高空间分辨率的嵌套网格）和不同的季节。通过调和“自下而上”模型和“自上而下”排放之间的差异，我们将获得一个更准确的估计有多少异戊二烯是从亚马逊排放，更重要的是获得一个更好的理解的因素，影响它的排放。这项研究很重要，因为亚马逊盆地也是被确定为最容易受到气候变化影响的地区之一，我们确定影响其异戊二烯排放的关键因素，以提高对我们预测未来气候能力的信心，这一点至关重要。

The term climate change is now a household phrase and we are used to hearing about rising greenhouse gas levels and global warming. One of the first events that increased the public's awareness of environmental issues was the discovery of the Antarctic ozone hole in the 1980s. Ozone is a gas that comprises only a tiny fraction of all the gases that make up the atmosphere but it is very important in climate. At high altitudes (about 15 to 30 km), there is lots of ozone which is good thing for the planet, as it shields the Sun's harmful UV radiation. However, ozone is a toxic substance and if it builds up within the troposphere (the lowermost part of the atmosphere) and at the surface then this is not good. Tropospheric ozone is bad for us because it is (a) a greenhouse gas, and (b) and air pollutant that affects the human respiratory system and agricultural crop yields. Ozone is produced near the surface when substances known as volatile organic compounds (VOCs) are emitted from the surface and subsequently react within the atmosphere. VOCs can be emitted from human activities, but they are predominantly emitted by vegetation that grows on land. Of all the biogenic VOCs emitted into the atmosphere, none is more important than isoprene owing to its ability to quickly react with other compounds (to produce tropospheric ozone) and because it is emitted in large amounts. Isoprene is also important, as it is a source of very small particles called secondary organic aerosol (SOA) that scatter light, which influences how the Earth warms, and which also have adverse health effects. We need to know (a) when, (b) where and (c) how much isoprene is emitted into the atmosphere in order to better understand tropospheric ozone and SOA. Currently we use generic computer models that are based on observations to simulate the amount of isoprene emitted from different types of vegetation, such as trees or grasses. Isoprene emissions from the Amazon Basin, which contains the world's largest rain forest and is thought to be one of the biggest isoprene sources, are poorly quantified since it is very difficult to measure the emissions in this largely inaccessible and remote region. Satellite observations of a gas called formaldehyde (HCHO), contain information on isoprene emissions, and can be used to determine the amount of isoprene emitted from terrestrial vegetation. The overall goal of my proposal is to use satellite observations of HCHO to accurately quantify isoprene emissions from the Amazon Basin. To achieve this goal I will develop a new unique high resolution model for the Amazon, which will be able to simulate isoprene emissions and atmospheric chemistry at finer spatial scales than have been able previously. I will then compare the isoprene emissions from this 'bottom-up' model with the 'top-down' isoprene emissions inferred from the satellite observations of HCHO, to identify regions or time periods where there is significant disagreement between the model and the observations, which highlights where we have poor understanding of the isoprene emissions. I will then develop an improved isoprene emission model by fine tuning the 'bottom-up' emission model to the inferred 'top-down' emissions, taking into account individual scenes (utilizing the high spatial resolution of the nested-grid) and different seasons. By reconciling the differences between the 'bottom-up' model and the 'top-down' emissions we will gain a more accurate estimate of how much isoprene is emitted from the Amazon, and more importantly gain a better understanding of the factors that influence when it is emitted. This research is important because the Amazon Basin is also one of the regions identified as being most susceptible to climate change, and it is crucial we determine the key factors that influence its isoprene emissions in order to improve confidence in our ability to predict future climate.