Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters
Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters
复制标题
DOI：
10.5194/acp-11-12253-2011
复制
发表时间：
2011-01-01
影响因子：
6.3
通讯作者：
Spracklen, D. V.
中科院分区：
文献类型：
--
作者：
Lee, L. A.;Carslaw, K. S.;Spracklen, D. V.
大气模型的敏感性分析是必要的，以确定导致模型预测不确定性的过程，通过比较驱动过程来帮助理解模型的多样性，并优先考虑研究。评估复杂模型中参数不确定性的影响具有挑战性，并且通常受到CPU约束的限制。在这里，我们提出了一个具有成本效益的应用方差为基础的敏感性分析，量化的3-D全球气溶胶模型的不确定参数的敏感性。一个高斯过程仿真器是用来估计跨多维参数空间的模型输出，使用的信息从一个小数目的模型运行在使用拉丁超立方空间填充设计选择的点。高斯过程仿真是一种贝叶斯方法，它使用来自模型运行的信息以及有关模型行为的一些先验假设，沿着来预测不确定性空间中任何地方的模型输出。我们使用高斯过程模拟器来计算全球气溶胶模型参数及其相互作用的不确定性所解释的预期输出方差的百分比。为了演示该技术，我们展示了云凝结核（CCN）的敏感性的例子，8个模型参数在污染和偏远的海洋环境中作为高度的函数。在污染环境中，CCN浓度的95%的方差由8个参数的不确定性（排除它们的相互作用）描述，并且由硫排放的不确定性主导，这解释了80%的方差。然而，在偏远地区的参数相互作用的影响变得重要，占总方差的40%。一些参数被证明有一个可以忽略不计的个人影响，但大量的相互作用的影响。在常用的单参数扰动实验中不会检测到这种敏感性，因此会低估总的不确定性。高斯过程仿真是一种有效和有用的技术，用于量化复杂的全球大气模型中的参数敏感性。
Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space, using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95% of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80% of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40% of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric models.