CAREER: Understanding the Predictability and Dynamics of Spring Onset in North America

春天的到来对农业和生态系统来说是一个关键的过渡，因为植物长叶，生长季开始了。每年的发病时间可能会有很大的不同，因此熟练的发病预测对农业规划将是有价值的。但是，春季疫情的可预测性，以及导致发病日期年复一年的差异的过程，还不是很清楚。这里进行的研究解决了这两个问题。发病显然与当地的气象因素有关，如阳光、温暖和降雨，但PI和合作者的初步工作表明，诸如丁香落叶等发病指标比相关的气象变量具有更大的可预测性。因此，基于观测记录的此类物候指标的可预测性是研究的重点。相关气象条件的可预测性也是这项研究的一个目标，该研究利用国家多模式组合(NMME)项目的长期预报来解决。通过一个新的众包追溯预报项目进一步分析发病的可预测性，在该项目中，天气研究和预报(WRF)模型为个人计算机配置，并向当地高中生传播。学生使用不同版本的模型物理来创建WRF预测，从而考虑到预测模型中固有的不确定性。集合预报方面的进一步工作将集合预报技术与一个简单的经验线性逆模式(LIM)提供的基线进行比较。LIM的比较确定了使用复杂的非线性预报模型相对于基于过去行为的统计方法的优势，并检验了可预测的过渡动态基本上是线性的假设。控制落叶和其他物候时间的当地气象变化反过来又受到春季迅速发生的大尺度大气环流重组的控制。特别是，太平洋中部和东部的急流分裂和向北迁移已被确定为一个关键因素。使用不同复杂程度的模型层级来研究喷流转换的动力学，包括具有规定的真实海洋表面温度(SSTs)的全球大气模型，去除陆地并简化了海洋表面温度的相同模型，以及线性斜压模型。这一职业奖的教育部分涉及使用WRF作为向20个地区高中的学生教授天气和气候科学的工具。国际和平研究所与古生物研究所的地球博物馆(MOE)合作，为教师举办关于使用WRF模式作为教学工具的培训讲习班。工作坊强调在实践中学习，互动地向教师展示他们的学生如何建立和运行模型，如何对附近的气象站进行每日预报，并在第二天验证预报。此外，教育活动是研究活动的一个组成部分，因为学生制作的世界气候变化框架预报被收集成集合，用于研究春季开始的可预测性。除了对教育的更广泛影响外，这项研究还具有更广泛的影响，因为巧妙地预测春季来临的潜在价值，这将使农民和农业部门的其他决策者受益。该项目为研究生提供支持和培训，为本科生提供博士后和暑期支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The onset of Spring is a critical transition for agriculture and ecosystems, as plants leaf out and the growing season begins. The timing of onset can vary considerably from year to year, thus skillful onset predictions would be valuable for agricultural planning. But the predictability of Spring onset, and the processes that lead to year-to-year differences in onset date, are not well known. Research conducted here addresses both issues. The onset is clearly linked to local meteorological factors such as sunlight, warmth, and rainfall, but preliminary work by the PI and collaborators suggests that onset indicators such as the leaf-out of lilacs have greater predictability than the associated meteorological variables. The predictability of such phenological indicators based on the observational record is thus a focus of the research. The predictability of the associated meteorological conditions is also a target of the research, addressed using long-range forecasts from the National Multi-Model Ensemble (NMME) project. Onset predictability is further analyzed through a new crowdsourced retrospective forecasting project, in which the Weather Research and Forecasting (WRF) model is configured for personal computers and disseminated to local high school students. Students create WRF forecasts using different versions of the model physics, thereby accounting for uncertainties inherent in the forecast model. Further work on ensemble prediction compares ensemble forecast skill to a baseline provided by a simple empirical linear inverse model (LIM). The LIM comparison determines the advantage of using complex nonlinear forecast models over statistical methods based on past behavior, and tests the hypothesis that the predictable dynamics of the transition are essentially linear.The changes in local meteorology that control the timing of leaf-out and other phenology are in turn controlled by a reorganization of the large-scale atmospheric circulation that occurs rapidly in Spring. In particular, the splitting and northward migration of the jet stream over the central and eastern Pacific have been identified as a key factor. The dynamics of the jet transition are examined using a hierarchy of models of varying degrees of complexity, including a global atmospheric model with prescribed realistic sea surface temperatures (SSTs), the same model with the landmasses removed and simplified sea surface temperatures, and a linear baroclinic model.The educational component of this CAREER award involves the use of WRF as a tool for teaching weather and climate science to students in 20 regional high schools. The PI works with the Paleontological Research Institution's Museum of the Earth (MoE) to conduct training workshops for teachers on the use of the WRF model as a teaching tool. The workshops emphasizes learning by doing, interactively showing the teachers how their students can set up and run the model, make daily forecasts for nearby weather stations, and validate the forecasts the following day. In addition, the educational activities are an integral part of the research activity, as the WRF forecasts produced by the students are collected into ensembles used for research on spring onset predictability. Aside from the educational broader impacts, the research has broader impacts due to the potential value of skillful predictions of spring onset, which would benefit farmers and other decision makers in the agricultural sector. The project provides support and training to a graduate student and a postdoc and summer support for an undergraduate.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.