CAREER: Elucidating Large-Scale Spatial Patterns of Ecosystem Traits with Data Assimilation

计算机模型用于对地球上的生命状态及其周围的大气进行全球预测。这些模型对全球气候以及与地球上的植物和微生物生命的联系做出重要预测。这些模型中的许多都依赖于地球上植物与大气之间的简单关系。该职业奖将探索发展这些非常重要的关系的新方法以及导致其差异的因素（土壤，光线，水等的变化）。由于没有足够的信息，在植被和土壤类型的空间变化，大多数模型假设，植被响应类型的变化只基于土地覆盖类型。过去的研究表明，其他众所周知的属性会影响植被的敏感性，例如特定位置的干燥程度，或者土壤中有多少粘土。该CAREER奖项将使用一个新的建模框架和卫星数据，以获得世界各地最佳工厂参数的地图，并测试这些关系，即使在现场测量很少的地区。该研究还将确定使用这些关系是否可以改善生态系统吸收二氧化碳量的模型预测。该奖项的结果将通过更准确地预测二氧化碳吸收，植物生长和土壤分解来改善生态系统如何应对气候变化的预测。此外，该奖项还包括高中生（教师培训）到本科生（包括重新设计课程材料和本科生研究经验）到大学后（创建一个关于将观察纳入模型的数学技术的研讨会）的几个教育组件。 大尺度陆地生态系统模式是气候变化预测中不确定性的主要来源之一。尽管几十年来一直在努力提高过程表示的复杂性，但它们仍然不确定。然而，很少有人关注参数优化。生态系统模型参数的分配仅仅基于少数植物功能类型，而没有考虑到地球仪上植物行为的巨大变化。该项目将测试一种形成植物功能类型替代品的新途径：使用数据同化。拟议的工作将使用CARbon数据模型框架（CARDAZH），它结合了一个简单的生态系统模型，遥感数据和马尔可夫链蒙特卡罗模拟，以确定生态系统参数，导致最现实的通量和碳池在整个地球仪的每个像素。由此产生的参数图不能直接用于其他模型，但将用于测试所谓的环境过滤关系，以预测生态系统参数的变异性的基础上，其空间变化是众所周知的其他因素（如气候，土壤类型等）。该奖项将测试是否可利用CARDATM中的遥感数据同化，采用类似于最近现场分析的方法，但不依赖现场测量的质量和数量（在热带等传统上采样不足的地区尤其成问题），得出整个地球仪的环境过滤关系。它还将创建和证明异养呼吸，其空间变异性不能单独由原位测量约束的这种关系的价值。 该项目的教育部分包括为初中和高中生物、化学和物理教师开发几个关于生态系统过程和气候变化主题的教学模块。该项目还将用于支持与CARDANOP.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Computer models are used to make global predictions about the state of life on earth and the atmosphere that surrounds it. These models make important predictions about global climate and the links to plant and microbial life on earth. Many of these models rely on simple relationships about plants on earth and their connections to the atmosphere. This CAREER award will explore new ways of developing these very important relationships and the factors (changes in soil, light, water, and more) that result in their differences. Because there is not enough information about spatial variations in vegetation and soil types, most models assume that vegetation response types vary only based on land cover types. Past research suggests that other well-known properties affect vegetation sensitivities, for e.g. how dry a particular location is, or how much clay the soil has. This CAREER award will use a new modelling framework together with satellite data to derive a map of optimal plant parameters around the world, and to test these relationships even in regions where field measurements are scarce. The research will also determine whether using these relationships can improve model predictions of how much carbon dioxide ecosystems absorb. The results of this award will improve predictions of how ecosystems respond to climatic changes by enabling more accurate predictions of carbon dioxide uptake, plant growth, and soil decomposition. Additionally, this award includes several educational components for high school students (teacher training) through undergraduates (including redesign of the material for a class, and undergraduate research experience) to post-collegiate (creating a workshop on mathematical techniques for incorporating observations into models). Large scale models of terrestrial ecosystems are one of the dominant sources of uncertainty in predictions of climate change. They have remained uncertain despite decades of effort to increase the sophistication of process representations. However, much less attention has been paid to parameter optimization. Ecosystem model parameters are assigned solely based on a handful of plant functional types, without accounting for the enormous variety of plant behavior across the globe. This project will test a new pathway for forming alternatives to plant functional types: using data assimilation. The proposed work will use the CARbon DAta MOdel fraMework (CARDAMOM), which combines a simple ecosystem model, remote sensing data, and Markov Chain Monte Carlo simulations to determine ecosystem parameters that result in the most realistic fluxes and carbon pools in each pixel across the globe. The resulting parameter maps cannot be used directly in other models but will be used to test so-called environmental filtering relationships to predict ecosystem parameter variability based on other factors whose spatial variation is well known (e.g. climate, soil type, etc). This award will test whether assimilating remote sensing data in CARDAMOM can be used to derive environmental filtering relationships across the globe using approaches similar to those from recent in situ analyses, but without relying on the quality and quantity of in situ measurements (particularly problematic in traditionally under-sampled regions like the tropics). It will also create and demonstrate the value of such relationships for heterotrophic respiration, whose spatial variability cannot be constrained by in situ measurements alone. The educational components of the project include development of several instructional modules on topics related to ecosystem processes and climate change for middle and high school biology, chemistry, and physics teachers. The project will also be used to support a bi-annual workshop on data assimilation with CARDAMOM.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.