DEPICT-SNOW: Decoding and predicting the change in future extreme snowfall in the Northern Hemisphere

DEPICT-SNOW：解码和预测北半球未来极端降雪的变化

• 批准号: EP/Y029119/1
• 负责人: Tim Woollings
• 金额: $ 25.55万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY029119%2F1
• 关键词: DEPICT; SNOW; Decoding; predicting; change

Extreme snowfall is a major disastrous climate event that has enormous economic and human impacts; however, according to the latest Assessment Report of the Intergovernmental Panel on Climate Change, there is low confidence in both past and future changes in heavy snowfall. This is due to highly uncertain atmospheric circulation response to climate change, model biases, poor snowfall observations and limited model ensembles. The overarching aim of DEPICTSnow is to pinpoint the controlling mechanisms for, and to produce innovative and timely predictions/projections of future extreme snowfall change in the Northern Hemisphere for the coming decades. DEPICTSnow will use an advanced weather forecast model, large-ensemble/high-resolution modelling strategy and advanced analytical methods to make significant advances in future extreme snowfall change and predictions/projections. These unprecedented large-ensemble, high-resolution weather forecast model simulations are enabled by the University of Oxford's e- Research Centre's unique and innovative distributed computing project (Climateprediction.net) and the extensive application of this computing project in extreme events attribution. These backbone seasonal simulations will be analyzed with advanced analytic methods such as decomposition of thermodynamics and dynamics and considering key atmospheric circulation patterns to make breakthroughs in controlling mechanisms for future extreme snowfall change. Innovative and timely predictions/projections of future extreme snowfall will be produced using both risk-based and storyline-based approaches, which complements each other. These can be of immediate importance to the scientific communities and wider public sectors. In short, DEPICTSnow will fill a significant knowledge gap in the controlling mechanisms of future extreme snowfall change, inform societies of quantitative risks associated with future extreme snowfall change and guide critical mitigation strategies.

极端降雪是一种重大的灾难性气候事件，具有巨大的经济和人类影响；然而，根据政府间气候变化专门委员会的最新评估报告，人们对大雪过去和未来的变化都缺乏信心。这是由于高度不确定的大气环流对气候变化的反应、模型偏差、糟糕的降雪观测和有限的模型集合。DEPICTSnow的首要目标是确定未来几十年北半球未来极端降雪变化的控制机制，并对其进行创新和及时的预测/预测。DEPICTSnow将使用先进的天气预报模式、大集合/高分辨率模拟策略和先进的分析方法，在未来极端降雪变化和预报/预测方面取得重大进展。这些史无前例的大集合、高分辨率天气预报模式模拟是由牛津大学电子研究中心独特的创新分布式计算项目(Climate atePrediction.net)以及该计算项目在极端事件归因中的广泛应用而实现的。这些主干季节模拟将使用热力学和动力学分解以及考虑关键大气环流形势等先进分析方法进行分析，以突破未来极端降雪变化的控制机制。对未来极端降雪的创新和及时的预测/预测将采用基于风险和基于故事情节的方法，两者相辅相成。这些对科学界和更广泛的公共部门可能具有直接的重要性。简而言之，DEPICTSnow将填补未来极端降雪变化控制机制方面的重大知识空白，向社会通报与未来极端降雪变化相关的量化风险，并指导关键的缓解战略。