Investigating multi-decadal climate variations in seasonal forecasts

研究季节性预测中的数十年气候变化

• 批准号: 2598747
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2598747
• 关键词: Investigating; multi; decadal; climate; variations

The ability to forecast weather and climate reliably on a seasonal timescale is of great societal and scientific interest. If floods, droughts, and rainfall onset can be reliably predicted a season or more in advance, the economic and social costs of these events can be significantly reduced. Examples include being able to better predict tropical droughts in northern South America, India, parts of southern Africa and Australie, and earlier warnings of tropical cyclones in the Philippines and other parts of southeast Asia. This is particularly topical as some extreme weather events increase in frequency and severity due to climate change.Seasonal predictive skill has improved markedly over the past century, with successive generations of forecast systems improving on the accuracy and length of forecasts that are possible. A significant source of predictability, especially in the tropics, is provided by El Niño Southern Oscillation (ENSO), an irregular periodic variation in winds and sea surface temperatures (SSTs) over the tropical eastern Pacific Ocean. It is the strongest mode of multi-annual atmospheric variability and affects air pressure, precipitation, and temperatures all over the world through its impact on atmospheric and oceanic circulation patterns, known as teleconnections. There is evidence that these teleconnections are not stationary but can change over time. There is generally lower predictive skill in other areas of the world, notably the North Atlantic, where there is a complex, seasonally dependent relationship between ENSO, the North Atlantic Oscillation (NAO) and the strength and position of the jet stream. Issues, such as the signal-to-noise paradox (climate models have a low signal-to-noise ratio, but nevertheless are able to skilfully predict observed climate variability) and the ENSO spring predictability barrier (the correlation between predictions and observations dramatically decreases in boreal spring), have been identified and are the subject of open research.This project aims to investigate and identify the large-scale atmospheric and oceanic dynamical reasons for the observed multi-decadal variations in forecast skill, helping to understand what factors influence such low-frequency fluctuations in the climate state and model forecast skill and, ultimately, how to improve seasonal forecasts.

在季节性时间表上可靠地预测天气和气候的能力具有极大的社会和科学兴趣。如果可以可靠地预测地板，干旱和降雨的发作，那么这些事件的经济和社会成本就可以大大减少。例子包括能够更好地预测南美北部，印度，南部非洲和澳大利亚的部分地区的热带干旱，以及菲律宾和东南亚其他地区的热带气旋的早期警告。这是尤其是主题的，因为由于气候变化，某些极端天气事件的频率和严重程度增加了。季节预测技能在过去的一个世纪中显着提高，几代人的预测系统成功地提高了可能的森林的准确性和长度。厄尔尼诺南部振荡（ENSO）提供了一个重要的可预测性，尤其是在热带地区，这是热带东部太平洋的风和海面温度（SST）的不规则周期性变化。这是多年大气变异性的强大模式，并通过其对大气和海洋循环模式的影响（称为远程连接）影响了世界各地的气压，精度和温度。有证据表明，这些遥相关不是静止的，但会随着时间的流逝而改变。在世界其他地区，尤其是北大西洋地区的预测技能通常较低，那里的ENSO，北大西洋振荡（NAO）与喷气流的强度和位置之间存在复杂的季节性关系。诸如信号到噪声悖论（气候模型的信噪比比率较低，但仍然能够时尚预测的气候可变性）和ENSO弹簧可预测性障碍（预测和观察结果之间的相关性（在鲍里弹簧中大大降低）的大规模研究和鉴定项目的范围较大的质量，诸如诸如信噪比的悖论（气候模型具有较低的信噪比），但已确定了大规模的研究。观察到的预测技能的多年差异变化，有助于了解哪些因素会影响气候状态和模型预测技能的低频波动，并最终如何改善季节性森林。