Investigating multi-decadal climate variations in seasonal forecasts

在季节性时间尺度上可靠地预报天气和气候的能力具有极大的社会和科学价值。如果洪水、干旱和降雨能够提前一个季度或更长时间可靠地预测，这些事件的经济和社会成本可以显著降低。例如，能够更好地预测南美洲北部、印度、南部非洲部分地区和澳大利亚的热带干旱，以及菲律宾和东南亚其他地区更早的热带气旋警告。由于气候变化导致一些极端天气事件的频率和严重程度增加，这一点尤其重要。在过去的一个世纪里，季节性预测技能已显著提高，连续几代预测系统都在可能的预测精度和预测持续时间方面有所改进。厄尔尼诺南方涛动(ENSO)是热带东太平洋上空风和海面温度的一种不规则周期性变化，这是一个重要的可预测性来源，特别是在热带地区。它是多年大气变率的最强模式，通过影响大气和海洋环流模式，影响世界各地的气压、降水和温度，称为遥相关。有证据表明，这些远程联系不是固定的，而是会随着时间的推移而变化。世界其他地区的预报能力普遍较低，特别是北大西洋，在那里，ENSO、北大西洋涛动(NAO)和急流的强度和位置之间存在复杂的、随季节变化的关系。一些问题，如信号噪声悖论(气候模式具有低信噪比，但仍然能够熟练地预测观测到的气候变异性)和ENSO春季可预测性障碍(北方春季预测和观测之间的相关性急剧下降)，已经被确定并成为开放研究的主题。该项目旨在调查和确定大尺度大气和海洋动力学原因，导致观测到的几十年来预测技能的变化，帮助了解哪些因素影响气候状态和模式预测技能中的这种低频波动，并最终了解如何改进季节性预测。

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.