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转变，因为植物长叶，生长季节开始了。发病时间每年都有很大的不同，因此熟练的发病预测对农业规划是有价值的。但春天开始的可预测性，以及导致每年开始日期差异的过程，尚不为人所知。这里进行的研究解决了这两个问题。这种现象的发生显然与当地的气象因素有关，如阳光、温暖和降雨，但PI及其合作者的初步工作表明，像紫丁香叶子脱落这样的发生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研究的一个目标，利用国家多模式集合（NMME）项目的长期预报来解决这个问题。通过一个新的众包回顾性预报项目，进一步分析了天气预报的可预测性，在该项目中，天气研究与预报（WRF）模型被配置为个人电脑，并传播给当地的高中学生。学生使用不同版本的模型物理创建WRF预测，从而考虑到预测模型中固有的不确定性。集合预测的进一步工作将集合预测技能与简单经验线性逆模型（LIM）提供的基线进行比较。LIM比较确定了使用复杂非线性预测模型比基于过去行为的统计方法的优势，并检验了过渡的可预测动态本质上是线性的假设。控制落叶时间和其他物候的当地气象变化，反过来又受到春季迅速发生的大规模大气环流重组的控制。特别是中太平洋和东太平洋上空急流的分裂和北移被认为是一个关键因素。使用不同复杂程度的模型层次来研究急流过渡的动力学，包括具有规定的实际海表温度（SSTs）的全球大气模型，去掉大陆块并简化海表温度的相同模型，以及线性斜压模型。该职业奖的教育部分涉及使用WRF作为工具，向20所地区高中的学生教授天气和气候科学。PI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世界自然资源基金模型作为教学工具的培训讲习班。工作坊强调边做边学，互动地向老师展示学生如何建立和运行模型，为附近的气象站做每日预报，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，因为学生们制作的WRF预测被收集到用于春季开始可预测性研究的集合中。除了更广泛的教育影响外，该研究还具有更广泛的影响，因为熟练预测春季开始的潜在价值，这将使农民和农业部门的其他决策者受益。该项目为一名研究生提供支持和培训，为一名本科生提供博士后和暑期支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.