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The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

机译：云凝结核整体模型模拟中不确定性的大小和原因

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Aerosol–cloud interaction effects are a major source of uncertainty inclimate models so it is important to quantify the sources of uncertainty andthereby direct research efforts. However, the computational expense of globalaerosol models has prevented a full statistical analysis of their outputs.Here we perform a variance-based analysis of a global 3-D aerosolmicrophysics model to quantify the magnitude and leading causes of parametricuncertainty in model-estimated present-day concentrations of cloudcondensation nuclei (CCN). Twenty-eight model parameters covering essentiallyall important aerosol processes, emissions and representation of aerosol sizedistributions were defined based on expert elicitation. An uncertaintyanalysis was then performed based on a Monte Carlo-type sampling of anemulator built for each model grid cell. The standard deviation around themean CCN varies globally between about ±30% over some marine regions to±40–100% over most land areas and high latitudes, implying that aerosolprocesses and emissions are likely to be a significant source of uncertaintyin model simulations of aerosol–cloud effects on climate. Among the mostimportant contributors to CCN uncertainty are the sizes of emitted primaryparticles, including carbonaceous combustion particles from wildfires,biomass burning and fossil fuel use, as well as sulfate particles formed onsub-grid scales. Emissions of carbonaceous combustion particles affect CCNuncertainty more than sulfur emissions. Aerosol emission-related parametersdominate the uncertainty close to sources, while uncertainty in aerosolmicrophysical processes becomes increasingly important in remote regions,being dominated by deposition and aerosol sulfate formation duringcloud-processing. The results lead to several recommendations for researchthat would result in improved modelling of cloud–active aerosol on a globalscale.
机译：气溶胶与云的相互作用是气候模型不确定性的主要来源，因此量化不确定性的来源并进行直接研究非常重要。然而，全球气溶胶模型的计算费用阻碍了对其输出的全面统计分析。在此，我们对全球3-D气溶胶微观物理模型进行了基于方差的分析，以量化当前模型估计中参数不确定性的大小和主要原因浓缩云的浓度（CCN）。根据专家的启发定义了28个模型参数，这些参数基本上涵盖了所有重要的气溶胶过程，排放和气溶胶尺寸分布的表示形式。然后基于为每个模型网格单元构建的仿真器的蒙特卡洛型采样执行不确定性分析。在全球范围内，一些海洋区域的标准差CCN周围的标准偏差在大约±30％到大多数陆地区域和高纬度的±40-100％之间变化，这暗示着气溶胶过程和排放可能是气溶胶模型模拟中不确定性的重要来源。云对气候的影响。导致CCN不确定性最重要的因素是排放的初级粒子的大小，包括野火，生物质燃烧和化石燃料的使用所产生的碳质燃烧粒子，以及在亚网格规模上形成的硫酸盐粒子。碳质燃烧颗粒的排放对CCN不确定性的影响大于硫排放。与气溶胶排放相关的参数主导着靠近源头的不确定性，而在偏远地区，气溶胶微物理过程的不确定性变得越来越重要，主要受云处理过程中的沉积和硫酸气溶胶形成的影响。结果为研究提出了一些建议，这些建议将导致在全球范围内改进云活性气溶胶的建模。

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