MesoS2D: Mesospheric sub-seasonal to decadal predictability

MesoS2D：中层次季节到年代际的可预测性

• 批准号: NE/V018426/1
• 负责人: Tracy Moffat-Griffin
• 金额: $ 148.46万
• 依托单位: British Antarctic Survey
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV018426%2F1
• 关键词: MesoS2D; Mesospheric; sub; seasonal; decadal

In order to accurately predict impacts of space weather and climate variability on the whole atmosphere we need an accurate representation of the whole atmosphere. The mesosphere (~50-95 km altitude) is the most poorly understood region of the atmosphere, it is the critical boundary between two domains (the climate domain and the space weather domain) and this presents a problem when trying to model and predict conditions in the whole atmosphere. Currently the level of prediction in the mesosphere is no better than climatology. Historically there have been few observations of this region to help us characterise it. However, in the past decade or so the number of observations has increased markedly, including multiple middle atmosphere observing satellite missions. We plan to take advantage of this golden age of middle atmosphere observations and together with one of the world most sophisticated whole atmosphere models to quantify the variability and drivers of the mesosphere. The mesosphere influences, and is influenced by, in-situ and external effects such as atmospheric waves and tides (upward) and space weather effects (downward). The mesosphere is strongly coupled to the lower edge of the ionosphere, as well as the other atmospheric regions, so changes in one part can impact on others. In order to make progress in modelling the whole atmosphere as a coupled system we need to have a sound scientific understanding of the drivers of variability. For climate models we have a good level of predictability for ~2 weeks and one the ~decades scale. However, critically we cannot do this in the mesosphere yet. We aim to focus our efforts on understanding variability on the sub-seasonal to decadal variations in the mesosphere as a pathway to improving model predictions. We will use the highly instrumented region of Scandinavia, in conjunction with satellite data, to determine the variability of the mesosphere/lower ionosphere and its drivers over a sub seasonal to decadal scale. We will be among the first to use a new, ~£50 million, high-resolution instrument (EISCAT 3D). This will be the world's most sophisticated ionospheric radar which will allow unprecedented small scale measurements of variations in the middle atmosphere. In conjunction with special high-resolution whole atmosphere model simulations, we will determine the drivers and variability of this atmospheric region and provide a first step along the road of improving predictability of the mesosphere at sub-seasonal to decadal timescales.

为了准确预测太空天气和气候变化对整个大气的影响，我们需要对整个大气的准确表示。中层（〜50-95 km高度）是大气中最鲜为人知的区域，它是两个域（气候域和太空天气域）之间的临界边界，在试图建模和预测整个大气中的条件时，这是一个问题。目前，中层的预测水平不比气候学好。从历史上看，对该地区的观察很少，可以帮助我们描述它。但是，在过去的十年左右的时间里，观察数的数量显着增加，包括多个中间大气观测卫星任务。我们计划利用这个中间大气观测的黄金时代，以及世界上最复杂的整个大气模型之一，以量化中层的可变性和驱动因素。中层影响，并受到原地和外部影响的影响，例如大气波和潮汐（向上）和太空天气效应（向下）。中层强烈耦合到电离层的下边缘以及其他大气区域，因此一个零件的变化会影响其他零件。为了在整个大气中建模为耦合系统，我们需要对变异性驱动因素有一个合理的科学理解。对于气候模型，我们在2周内具有良好的可预测性水平，而数十年的标准范围为一个。但是，在批判性的情况下，我们还不能在Mesosphere中这样做。我们旨在将努力集中在理解下季节的变异性上，以作为改善模型预测的途径，以纪念际变化。我们将使用斯堪的纳维亚半岛的高度仪器区域与卫星数据结合使用，以确定在亚季节到衰老尺度上的中层/下部电离层及其驱动因素的变异性。我们将是最早使用新的约5000万英镑高分辨率工具（Eiscat 3D）的人之一。这将是世界上最复杂的电离层雷达，它将允许对中间大气中变化的前所未有的小规模测量。结合特殊的高分辨率整个大气模型模拟，我们将确定这个大气区域的驱动因素和变异性，并沿着改善衰老时间尺度下季节的中层可预测性的第一步。