Long-term variation characteristics and influencing factors of low-visibility events on the coast of China

Abstract Low-visibility events (LVEs, e.g., haze, fog, mist) in China have comprised a high proportion of bad weather phenomena in past decades. In this study, the long-term (1980–2017) trend of LVEs over the Chinese coastland and its influencing factors were examined using in-situ meteorological and reanalysis data as well as climatic indices data. A stricter definition of LVEs was proposed to investigate the events occurring on a short sub-day time scale. Higher frequencies of LVEs in ports were possibly induced by local ship emissions. The numbers of annual fog and haze days exhibited almost opposite temporal variation trends from 1980 to 2017, and the transition of haze days towards fog days occurred around 2004. Notably, the coexistence or conversion of fog and haze events was always found within one day. Our analysis showed that the dominant factor influencing haze towards fog days was not aerosol change in low atmosphere, especially in the last decade, but relative humidity (RH) variation, which was affected by air temperature (T), sea temperature (SST), wind field, planetary boundary layer height (PBLH), and climate factors. From the perspective of LVE long-term changes, higher temperatures promote haze event occurrence, temperature inversion favors pollutant and moisture accumulation, high wind speeds significantly dissipate both fog and haze, and lower boundary layer accumulate water vapor effectively, enhancing the conversion of haze to fog. The influence of sea temperature indices on low-visibility days (LVDs) was found strongest. The temperature difference between sea and air would determine whether the formation of fog was caused by turbulent cooling or thermal uplift. The effects of atmospheric circulation on LVDs were manifold, mainly reflected in water vapor transport, pollutant transport and dissipation. The results can provide insights into long-term variations of LVEs and serve as a reference for accurately predicting small-scale fog and haze.