Improving estimates of ocean primary productivity:Coupling bio-optics into a semi-Lagrangian model of phytoplankton physiology and ocean mixing

改进海洋初级生产力的估计：将生物光学耦合到浮游植物生理学和海洋混合的半拉格朗日模型中

• 批准号: NE/D00960X/1
• 负责人: Helen Kettle
• 金额: $ 29.24万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FD00960X%2F1
• 关键词: Improving; estimates; ocean; primary; productivity

There are increasing amounts of carbon dioxide (CO2) in our atmosphere from the burning of fossil fuels and changes in land use. Because of the 'greenhouse effect' our climate is getting warmer. To predict how much warmer our planet will become in the future we need to understand all the processes on Earth that affect climate - these occur not only in the atmosphere but also on the land and in the sea. Once we have done this we can build computer models (i.e. write computer programs) which simulate the interactions between all these processes and then tell us what our climate might be like in the future. However, there are many complicated biological, chemical and physical processes that we don't yet fully understand so we still need to collect lots of data and spend time examining individual systems before we can properly model the whole climate system. The system I propose to look at concerns biological processes in the oceans. The oceans influence climate due to the movement of heat and gas between the air and sea. The amount of CO2 in the atmosphere at the moment is out of balance with that in the sea so the CO2 in the air is currently being absorbed by the oceans. This is good news for us because if there is more CO2 in the ocean, and less in the atmosphere, then the rate of global warming will be slower. Once the CO2 is dissolved in the oceans it may sink down to the deep ocean when the surface water cools and sinks, or it may get transported downwards through marine biology. And once it gets there it may not return to the surface for thousands of years. If the CO2 stayed at the sea surface it would hinder the progress of more CO2 entering the sea so it is important that mechanisms exist for transporting it to the deep ocean, otherwise we would have more CO2 in the atmosphere than we do now. I propose to model how the marine biology converts the CO2 dissolved in the surface waters to plant matter. This is the first stage in the biological transport of atmospheric CO2 to the deep ocean and so it is crucial that we understand it fully. There are lots of tiny floating plants ('phytoplankton') in the ocean, and as long as they have nutrient and sunlight they can absorb the dissolved CO2 from the sea water and use it to grow. This process is known as photosynthesis or carbon fixation. These plants may then be eaten by small drifting animals, fish or even whales, thus moving the carbon up the food chain. If these plants, animals or fish die, they may sink from the surface to the deep ocean, trapping the carbon from the atmosphere deep within the ocean interior. Much of what we know about these little plants comes from measurements taken by satellites orbiting the Earth. They can measure the amount of sunlight reflected from the ocean surface - this is known as 'ocean colour' - and it tells us how much plant life is near the sea surface. The research I propose, involves developing a computer model to investigate photosynthesis. It is not a simple process to model because the little plants move up and down through the water as the sea water mixes or stratifies (caused by changes in wind and sunlight). This is important because the deeper the little plants go, the less light they have but the more nutrient. The more light and nutrient they have - the faster they can photosynthesize. To get a handle on this process I will link the amount of plant life and the amount of carbon fixed, to ocean colour. This is good for 2 reasons. First, our model results will help us to understand exactly what the satellite ocean colour data is telling us about the state of the marine ecosystem, and second if we want to start adding ocean colour data into our models then we will know how to do it. All this is important because it will help us to understand what is actually happening in the oceans at the moment and that will help us to predict what will happen in the future.

由于化石燃料的燃烧和土地利用的变化，我们大气中的二氧化碳（CO2）越来越多。由于“温室效应”，我们的气候正在变暖。为了预测我们的星球在未来会变得多温暖，我们需要了解地球上影响气候的所有过程-这些过程不仅发生在大气中，而且发生在陆地和海洋中。一旦我们做到了这一点，我们就可以建立计算机模型（即编写计算机程序），模拟所有这些过程之间的相互作用，然后告诉我们未来的气候可能是什么样的。然而，有许多复杂的生物，化学和物理过程，我们还没有完全理解，所以我们仍然需要收集大量的数据，并花时间检查单个系统，然后才能正确地模拟整个气候系统。我建议研究的系统涉及海洋中的生物过程。由于空气和海洋之间的热量和气体的运动，海洋影响气候。目前大气中的二氧化碳含量与海洋中的二氧化碳含量不平衡，因此空气中的二氧化碳目前正在被海洋吸收。这对我们来说是个好消息，因为如果海洋中的二氧化碳更多，而大气中的二氧化碳更少，那么全球变暖的速度就会更慢。一旦二氧化碳溶解在海洋中，当表层水冷却并下沉时，它可能会沉入深海，或者它可能会通过海洋生物向下运输。一旦到达那里，它可能几千年都不会回到地面。如果二氧化碳停留在海面上，它将阻碍更多的二氧化碳进入海洋，因此重要的是存在将其输送到深海的机制，否则我们大气中的二氧化碳将比现在更多。我建议模拟海洋生物如何将溶解在表面沃茨中的二氧化碳转化为植物物质。这是大气中二氧化碳向深海生物运输的第一阶段，因此我们充分了解它至关重要。海洋中有许多微小的漂浮植物（“浮游植物”），只要它们有营养和阳光，它们就可以从海水中吸收溶解的二氧化碳并利用它来生长。这个过程被称为光合作用或碳固定。这些植物可能会被小型漂流动物、鱼类甚至鲸鱼吃掉，从而将碳转移到食物链的上游。如果这些植物、动物或鱼类死亡，它们可能会从海洋表面沉入深海，将大气中的碳捕获在海洋内部深处。我们对这些小植物的了解大部分来自绕地球运行的卫星的测量。他们可以测量从海洋表面反射的阳光量-这被称为“海洋颜色”-它告诉我们有多少植物生活在海面附近。我提出的研究，包括开发一个计算机模型来研究光合作用。这不是一个简单的建模过程，因为当海水混合或分层（由风和阳光的变化引起）时，小植物在水中上下移动。这很重要，因为小植物越深，它们的光线就越少，但营养就越多。它们拥有的光和营养越多，它们的光合作用就越快。为了掌握这个过程，我将把植物的数量和固定的碳的数量与海洋的颜色联系起来。这是好的有两个原因。首先，我们的模型结果将帮助我们准确地理解卫星海洋颜色数据告诉我们的海洋生态系统的状态，第二，如果我们想开始将海洋颜色数据添加到我们的模型中，那么我们将知道如何做到这一点。所有这些都很重要，因为它将帮助我们了解目前海洋中实际发生的情况，并帮助我们预测未来会发生什么。未来

项目成果

Lochnagar: The Natural History of a Mountain Lake

洛赫纳加尔：高山湖泊的自然历史

• 发表时间: 2007
• 作者: Kettle H,;R Thompson
• 通讯作者: R Thompson

Sensitivity analysis of an Ocean Carbon Cycle Model in the North Atlantic: an investigation of parameters affecting the air-sea CO<sub>2</sub> flux, primary production and export of detritus

北大西洋海洋碳循环模型的敏感性分析：影响海气 CO 的参数研究

• DOI: 10.5194/osd-7-1977-2010
• 发表时间: 2010
• 作者: Scott V

Using satellite-derived backscattering coefficients in addition to chlorophyll data to constrain a simple marine biogeochemical model

除了叶绿素数据之外，还使用卫星衍生的后向散射系数来约束简单的海洋生物地球化学模型

• DOI: 10.5194/bg-6-1591-2009
• 发表时间: 2009
• 期刊: Biogeosciences
• 影响因子: 4.9
• 作者: Kettle H