资源环境科技发展态势分析平台

We examine past and future changes in both winter haze and clear weather conditions over the North China Plain (NCP) using a perturbed parameter ensemble (PPE) and elucidate the influence of model physical parameterizations on these future projections for the first time. We use a large-scale meteorology-based haze weather index (HWI) with values >1 as a proxy for haze-conducive weather and HWI $<-\mathrm{1}$ for clear weather conditions over the NCP. The PPE generated using the UK Met Office's HadGEM-GC3 model shows that under a high-emission (RCP8.5) scenario, the frequency of haze-conducive weather (HWI >1) is likely to increase whereas the frequency of clear weather (HWI $<-\mathrm{1}$) is likely to decrease in the future with a growing influence of climate change over the 21st century. Nevertheless, a reduction in the frequency of haze-conducive weather and increment in the frequency of clear weather, though less likely, is also possible. In the future, the frequency of haze-conducive weather for a given winter could be as much as ∼3.5 times higher than the frequency of clear weather over the NCP. More frequent haze-conducive weather (HWI >1) during winter over the NCP is found to be associated with an enhanced warming of the troposphere and weaker northwesterlies in the mid-troposphere over the NCP. We also examined the changes in the interannual variability of the haze-conducive and clear weather and found no marked changes in the variability during future periods. We find a clear influence of model physical parametrizations on climatological mean frequencies for both haze-conducive and clear weather. For the mid- to late 21st century (2033–2086), the parametric effect can explain up to ∼80 % of the variance in the climatological mean frequencies of PPE members. This shows that different model physical parameterizations lead to a different evolution of the model's mean climate, particularly towards the end of the 21st century. Therefore, it is desirable to consider the PPE in addition to the initialized and multimodel ensembles to obtain a more comprehensive range of plausible future projections.