Scales and Scaling in the Climate System

气候系统的尺度和尺度

• 批准号: RGPIN-2016-04796
• 负责人: Lovejoy, Shaun
• 金额: $ 2.4万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=685006
• 关键词: Scales; Scaling; Climate; System

The climate has traditionally been viewed as a composite of processes acting each over relatively narrow ranges of time scales. The most obvious are those associated with the daily and annual cycles, but there are many others: for example El Nino and the orbital (Milankovitch) cycles associated with the ice ages. The “in between” scales are are uninteresting “background noises”. However, most of the variability is in these wide scale range (“scaling”) processes: they are foreground, not background processes. This proposal is centred on re-establishing the primacy of these scaling processes and examining the consequences for understanding the climate of the past, present and future.***Understanding the past climate is largely a question of interpreting the paleo “proxy” climate records from ice and ocean cores, to tree rings and lake sediments. This proposal will use new “scaling fluctuation analysis” techniques to systematically understand and model the variability over time scales from centuries to millions of years, over spatial scales from the size of the planet to hundreds of kilometers. This is needed in order to reconstruct the past.***For the present climate, the proposal exploits the “butterfly effect” to reduce the weather to random noise driving a system with a long term memory. The butterfly effect refers to the fact that the atmosphere is so unstable that a butterfly flapping its wings can change its course. While the "butterfly effect" limits the accuracy of weather forecasts these have little skill beyond ten days at scales of months and longer the weather effectively gives random “nudges” to the state of the atmosphere and the statistics of the nudges combined with the huge system memory can be used to make long range forecasts. It turns out that the atmosphere's memory is so strong that we are still feeling the effects of century old fluctuations, and this memory can be exploited, thus improving the notoriously poor seasonal forecasts. ***The future climate depends not only on the natural variability associated with the long range memory and butterfly effect, it depends on the atmospheric composition especially greenhouse gases - land use and other anthropogenic changes. Currently, numerical climate models disagree significantly with each about the projections to 2050 or 2080. The existence of a new qualitatively different source of future projections is needed in order to give us confidence in future emission scenarios.***In the next five years, for past, present and future climate we aim to a) produce new multiproxy reconstructions, b) produce new monthly, seasonal, annual and decadal forecasts, c) produce new temperature and precipitation projections for the 21st century. bypass the model uncertainties by making projections without the models by exploiting the past historical data. **

传统上，气候被看作是在相对较窄的时间尺度范围内作用的过程的组合。最明显的是那些与日周期和年周期有关的，但还有许多其他的：例如厄尔尼诺现象和与冰期有关的轨道（米兰科维奇）周期。“中间”音阶是无趣的“背景噪音”。然而，大多数可变性是在这些宽范围（“缩放”）过程中：它们是前景，而不是背景进程。该提案的核心是重新建立这些尺度过程的首要地位，并检查对理解过去、现在和未来气候的影响。了解过去的气候在很大程度上是一个解释古“代理”气候记录的问题，从冰和海洋核到树木年轮和湖泊沉积物。该提案将使用新的“尺度波动分析”技术，系统地理解和模拟从几个世纪到数百万年的时间尺度上的变化，从地球大小到数百公里的空间尺度上的变化。这是重建过去所需要的。***对于目前的气候，该方案利用“蝴蝶效应”将天气减少为随机噪音，驱动具有长期记忆的系统。蝴蝶效应指的是大气非常不稳定，以至于一只蝴蝶扇动翅膀就能改变它的飞行路线。虽然“蝴蝶效应”限制了天气预报的准确性，但在几个月或更长时间的尺度上，这些预报在10天以上几乎没有技巧，但天气有效地对大气状态提供了随机的“推力”，而这些“推力”的统计数据与巨大的系统内存相结合，可用于进行长期预报。事实证明，大气的记忆是如此强大，以至于我们仍然能感受到百年来波动的影响，而这种记忆可以被利用，从而改善众所周知的糟糕的季节性预报。未来气候不仅取决于与长期记忆和蝴蝶效应相关的自然变率，还取决于大气成分，特别是温室气体——土地利用和其他人为变化。目前，数值气候模式对2050年或2080年的预测与上述两种模式都存在显著差异。为了使我们对未来的排放情景有信心，需要有一种新的、性质不同的未来预测来源。***在未来五年，我们的目标是：a)对过去、现在和未来的气候进行新的多代理重建，b)进行新的月度、季节、年度和10年预测，c)对21世纪进行新的温度和降水预测。通过利用过去的历史数据，在没有模型的情况下进行预测，从而绕过模型的不确定性。**