Estimations of aerosol radiate forcing through new microphysical processes and regime-based constraints

通过新的微物理过程和基于状态的约束来估计气溶胶辐射强迫

• 批准号: 1936979
• 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: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1936979
• 关键词: Estimations; aerosol; radiate; forcing; through

Large uncertainties exist for the indirect radiative forcing produced by aerosol-cloud interactions, and these uncertainties cast some doubt on the reliability of future climate models. Within these models there are a large number of parameters which cannot be related directly to observed quantities, and these parameters are often chosen in an ad hoc manner to provide the best match of historical climate simulations with satellite observations. There are two approaches I hope to take in order to constrain these parameters. The first approach to help narrow down these uncertainties would be to create a hierarchy of parameters based on the direct impact of each parameter on the radiative forcing output of a climate model. This can be done through a sensitivity analysis, relating the response of key cloud-based observables (such as liquid water path, cloud optical depth, and cloud droplet number concentration), to changes in various model parameters. However, when performed as a blanket analysis on a global scale, this may produce spurious results. This is because responses may differ between cloud regimes, owing to different atmospheric conditions both surrounding and within the cloud. Therefore, it is proposed to begin with a regime-based study, where model output is processed to classify clouds into various regimes. This can be done in the standard way, by binning clouds by cloud-top-pressure and cloud optical depth (with resultant regimes roughly analogous to standard cloud classifications). Alternatively, there may exist other classification methods (novel or otherwise) that may provide significant findings. Afterwards, a sensitivity analysis can then be performed on a regime-by-regime basis. This analysis would hopefully reveal which model parameters provide the strongest feedbacks to net-radiative forcing, allowing focus to be directed towards these variables in question. On top of this, said parameters may also have strong effects on other processes such as precipitation efficiency, which can in turn have net effects on inbound radiation. The second approach that could be taken would be to reverse-engineer parameter constraints using available satellite data. The climate model that will be used is the MET office HadGEM3 model, which comes with the CFMIP Observational Simulator Package (COSP). This package provides simulated model diagnostics under the simulated climatic conditions in the model, as would be retrieved from various satellites such as MODIS. By comparing the output from COSP with available satellite data from the last 20-30 years, it may be possible to identify any large disparities and work backwards to find the root cause of these differences. Although this process requires an in-depth knowledge of the algorithms that COSP is comprised of, I believe it would be possible to develop this process under the time constraints. By understanding the main cause of the disparities, the model can be specifically tuned to match the satellite observations, by placing emphasis on the parameters deemed most important by the reverse-engineering, rather than using a trial and error approach.

气溶胶-云相互作用产生的间接辐射强迫存在很大的不确定性，这些不确定性使人们对未来气候模式的可靠性产生怀疑。在这些模式中，有大量的参数不能直接与观测到的数量相关，这些参数往往是以一种特别的方式选择的，以提供历史气候模拟与卫星观测的最佳匹配。为了限制这些参数，我希望采取两种方法。第一种有助于缩小这些不确定性的方法是根据每个参数对气候模式辐射强迫输出的直接影响建立一个参数层次。这可以通过敏感性分析来完成，将关键的基于云的可观测量（如液态水路径，云光学深度和云滴数浓度）的响应与各种模型参数的变化联系起来。然而，当在全球范围内进行全面分析时，这可能会产生虚假的结果。这是因为云的周围和云内的大气条件不同，不同的云的响应可能不同。因此，建议开始与regime-based研究，其中模式输出进行处理，以分类到各种制度的云。这可以用标准的方法来完成，通过云顶气压和云的光学深度来划分云的区域（结果大致类似于标准的云分类）。或者，可能存在其他分类方法（新的或其他），可以提供重要的发现。然后，可以逐个制度进行敏感性分析。这一分析有望揭示哪些模型参数对净辐射强迫提供了最强的反馈，从而使人们能够将注意力集中在这些变量上。除此之外，所述参数还可能对其他过程（如降水效率）产生强烈影响，而降水效率又可能对入境辐射产生净影响。可以采取的第二种办法是利用现有卫星数据对参数限制进行逆向工程。将使用的气候模式是气象局HadGEM 3模式，该模式与CFMIP观测模拟器包（COSP）一起提供。该软件包提供了在模型中的模拟气候条件下的模拟模型诊断，如从中分辨率成像分光仪等各种卫星上检索的那样。通过将COSP的输出与过去20-30年的现有卫星数据进行比较，有可能确定任何大的差异，并反向寻找这些差异的根本原因。虽然这个过程需要深入了解COSP所包含的算法，但我相信在时间限制下开发这个过程是可能的。通过了解差异的主要原因，可以通过强调逆向工程认为最重要的参数，而不是使用试错法，对模型进行专门调整，以匹配卫星观测。