Abstract. With the increase in economic development over the past thirty years, many large cities in eastern and southwestern China are experiencing increased haze events and atmospheric pollution, causing significant impacts on the regional environment and even climate. However, knowledge on the aerosol physical and chemical properties in heavy haze conditions is still insufficient. In this study, two winter heavy haze events in Beijing that occurred in 2011 and 2012 were selected and investigated by using the ground-based remote sensing measurements. We used a CIMEL CE318 sunsky radiometer to retrieve haze aerosol optical, physical and chemical properties, including aerosol optical depth (AOD), size distribution, complex refractive indices and aerosol fractions identified as black carbon (BC), brown carbon (BrC), mineral dust (DU), ammonium sulfate-like (AS) components and aerosol water content (AW). The retrieval results from a total of five haze days showed that the aerosol loading and properties during the two winter haze events were comparable. Therefore, average heavy haze property parameters were drawn to present a research case for future studies. The average AOD is about 3.0 at 440 nm, and the Ångström exponent is 1.3 from 440 to 870 nm. The fine-mode AOD is 2.8 corresponding to a fine-mode fraction of 0.93. The coarse particles occupied a considerable volume fraction of the bimodal size distribution in winter haze events, with the mean particle radius of 0.21 and 2.9 µm for the fine and coarse modes respectively. The real part of the refractive indices exhibited a relatively flat spectral behavior with an average value of 1.48 from 440 to 1020 nm. The imaginary part showed spectral variation, with the value at 440 nm (about 0.013) higher than the other three wavelengths (about 0.008 at 675 nm). The aerosol composition retrieval results showed that volume fractions of BC, BrC, DU, AS and AW are 1, 2, 49, 15 and 33 %, respectively, on average for the investigated haze events. The preliminary uncertainty estimation and comparison of these remote sensing results with in situ BC and PM 2.5 measurements are also presented in the paper.

No abstract

Summer precipitation plays critical roles in the energy balance and the availability of fresh water over eastern China. However, little is known regarding the trend in local‐scale precipitation (LSP). Here we developed a novel method to determine LSP events in the summer afternoon throughout eastern China from 1970 to 2010 based on hourly gauge measurements. The LSP occurrence hours decrease at an annual rate of 0.25%, which varies considerably by region, ranging from 0.14% over the Yangtze River Delta to 0.56% over the Pearl River Delta. This declining frequency of LSP is generally accompanied by an increase in rain rate of LSP but a decrease in visibility, whose linkage to LSP events was investigated. In particular, more LSP events tended to form when the atmosphere was slightly polluted. Afterward, LSP was suppressed. These findings have important implications for improving our understanding of the climatology of daytime precipitation at local scales.

Seasonal dependence of initial error growth for El Niño‐Southern Oscillation (ENSO) in Zebiak‐Cane model is investigated by using a new approach, i.e. conditional nonlinear optimal perturbation (CNOP). It is found that CNOP‐type error tends to have a significant season‐dependent evolution, and produces most considerable negative effects on the forecast results. Therefore, CNOPs are closely related to spring predictability barrier (SPB). On the other hand, some other kinds of initial errors, whose patterns are different from those of CNOPs, have also been found. Although the magnitudes of such initial errors are the same as those of CNOPs in terms of the chosen norm, they either show less prominent season‐dependent evolutions, or have trivial effect on the forecast results, and consequently do not yield SPB for El Niño events. The results of this investigation suggest that the CNOP‐type errors can be considered as one of candidate errors that cause the SPB. If data assimilation or (and) targeting observation approaches possess the function of filtering the CNOP‐type or (and) other similar errors, it is hopeful to improve the prediction skill of ENSO.