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High-resolution climate dynamics

High-resolution climate dynamics
高分辨率气候动态
批准号:
NE/G010706/1
负责人:
Manoj Joshi
金额:
$3.37万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2009
资助国家:
英国
项目状态:
已结题
起止时间:
2009 至 --

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中文摘要
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英文摘要
Many processes that influence surface climate have their origins in the stratosphere, the layer of the atmosphere 15-40km above the ground. For instance, ozone changes caused by changes in solar output can lead to significant perturbations to weather and climate nearer the surface. Most current climate models do not properly represent the stratosphere, and therefore cannot properly represent this impact of these changes. In addition, the communication of the effects of stratospheric changes to the surface in models may depend on their ability to represent small-scale weather systems. The current generation of climate models can only just represent such small scale features. Ideally, one would examine the effects of such stratospheric forcings using a model which fully represents both the stratosphere and small-scale tropospheric synoptic systems, but such a model would be prohibitively expensive computationally. We therefore intend to use a slightly different approach in this study: we will develop a high-resolution, stratosphere-resolving climate model which has simplified representations of processes such as convection and land-surface processes. This model is significantly less computationally intensive than a state-of-the-art climate model, but is still capable of representing those dynamical and physical processes that are important in this regard. Using this model we intend to study how the climate of a period known as the Maunder minimum, when solar output was slightly lower than now, differed from the present. In particular, we intend to examine the differences between the results from our high-resolution model and those from more standard models. The results of this study will help us to better predict the regional surface impacts of future stratospheric changes including those caused by changes in solar output.
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Machine learning approaches to constrain and understand the role of clouds in climate change (ML4CLOUDS)
  • 批准号:
    NE/V012045/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $82.87万
  • 财政年份:
    2022
  • 负责人:
    Manoj Joshi
  • 依托单位:
Robust Spatial Projections of Real-World Climate Change
  • 批准号:
    NE/N018397/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $51.4万
  • 财政年份:
    2016
  • 负责人:
    Manoj Joshi
  • 依托单位:
国内基金
海外基金
发展/减排路径(SSPs/RCPs)下中国未来人口迁移与集聚时空演变及其影响
  • 批准号:
    19ZR1415200
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2019
  • 负责人:
    夏海斌
  • 依托单位:
红树林生态系统对气候异常变化的响应与适应