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.

在季节性时间表上预测天气和气候可靠的能力是伟大的社会和科学兴趣。菲律宾和南部的其他部分，由于气候变化而增加，由厄尔尼诺南部振荡（ENSO）证明，这是风和海面温度的周期性变化（SSTS）N Pacific是多年大气变异的最强模式。通过对大气和循环模式的影响，称为远程连接。 ENSO，北大西洋的振荡和强度和位置，他的射流问题，例如信号到噪声悖论（气候模型的信号比率较低，但仍要熟练地预测气候变化） ENSO春季预测性屏障和观测之间的北极弹簧中的障碍物大大降低，旨在调查和确定观察到的大气动力学原因，以使观察到的多年阶段变化在前播种中的多年变化。 Stele和模型预测技能以及如何证明季节性预测。