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.

为了准确地预测空间天气和气候变率对整个大气的影响，我们需要一个准确的代表整个大气。中间层（约50-95公里高度）是大气层中了解最少的区域，它是两个领域（气候领域和空间天气领域）之间的关键边界，这在试图模拟和预测整个大气层的条件时会出现问题。目前，中间层的预测水平并不比气候学好。从历史上看，对这一区域的观测很少，但在过去十年左右的时间里，观测次数显著增加，其中包括多个中层大气观测卫星任务。我们计划利用中层大气观测的这一黄金时代，并与世界上最复杂的整体大气模型之一一起量化中层大气的可变性和驱动因素。中间层影响着大气波和潮汐（向上）以及空间天气效应（向下）等原地效应和外部效应，并受到这些效应的影响。中间层与电离层的下缘以及其他大气区域密切耦合，因此一个部分的变化可能会影响其他部分。为了在将整个大气作为一个耦合系统进行建模方面取得进展，我们需要对可变性的驱动因素有一个合理的科学认识。对于气候模式，我们有一个良好的预测水平为~2周和一个~几十年的规模。然而，关键的是，我们还不能在中间层做到这一点。我们的目标是把我们的努力集中在了解中间层的亚季节到年代际变化的变化，作为改善模式预测的途径。我们将使用高度仪表化的斯堪的纳维亚地区，结合卫星数据，以确定中间层/低电离层及其驱动程序在一个子季节到十年尺度的变化。我们将是第一批使用新的，~ 5000万英镑，高分辨率仪器（EISCAT 3D）。这将是世界上最先进的电离层雷达，它将允许对中层大气的变化进行前所未有的小规模测量。结合特殊的高分辨率全大气模式模拟，我们将确定这一大气区域的驱动因素和变化，并提供了第一步沿着提高中间层的可预测性的道路在亚季节到十年的时间尺度。