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Rossby waves can cross the equator and connect the Northern Hemisphere (NH) and Southern Hemisphere (SH), or be blocked in the vicinity of the equator. This work explores the windows and barriers for the cross-equatorial waves (CEWs) by the wave ray ensemble method. The eastern Pacific and Atlantic regions are identified as common windows in both boreal winter and summer, while the Africa–Indian Ocean section exists as a window only in boreal summer. The western–central Pacific is found to be a barrier section. These results are consistent with correlation analysis of reanalysis data. Moreover, the dependence on the wavenumber of CEWs is investigated, revealing that they are restricted to long waves with zonal wavenumbers less than 6 and that their wavenumber vectors exhibit a northwest–southeast (southwest–northeast) tilt when they cross the equator from the NH to SH (from the SH to NH). This long-wave dominance of CEWs results from the spectral-selective filtering mechanism, which suggests that long waves have narrower equatorial barriers than short waves. Finally, the main wave duct associated with each window is obtained by the global passing CEW density distribution. The results indicate that the main CEW ducts roughly follow a great circle–like pathway, except for the Africa–Indian Ocean window in boreal summer, which may be modulated by the cross-equatorial monsoonal flow.

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The production of secondary organic aerosols (SOA) from toluene photochemistry in Shanghai, a megacity of China, was estimated by two approaches, the parametrization method and the tracer-based method. The temporal profiles of toluene, together with other fifty-six volatile organic compounds (VOCs), were characterized. Combing with the vapor wall loss corrected SOA yields derived from chamber experiments, the estimated toluene SOA by the parametrization method as embodied in the two-product model contributes up to ∼40% of the total SOA budget during summertime. 2,3-Dihydroxy-4-oxopentanoic acid (DHOPA), a unique product from the OH-initiated oxidation of toluene in the presence of elevated NO x , was used as a tracer to back calculate the toluene SOA concentrations. By taking account for the effect of gas-particle partitioning processes on the fraction of DHOPA in the particle phase, the estimated toluene SOA concentrations agree within ∼33% with the estimates by the parametrization method. The agreement between these two independent approaches highlight the need to update current model frameworks with recent laboratory advances for a more accurate representation of SOA formation in regions with substantial anthropogenic emissions.

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Stagnation weather affects atmospheric diffusion ability, and hence causes the occurrence of haze events, which have been happening frequently in northern China (NC). This work puts forward an air stagnation index (ASITS) to characterize the stagnation weather in NC, in which the processes of ventilation, vertical diffusion, and wet deposition potency are concerned. ASITS can be applied to analyze air stagnation conditions with daily to monthly time scale. It is shown that the ASITS and particulate matter smaller than 2.5 μm in diameter (PM2.5) concentrations own similar lognormal probability distribution functions on both daily and monthly time scales. And the correlation analyses between the ASITS and PM2.5 concentrations indicate that the ASITS can reflect the monthly and daily variations in PM2.5 concentrations in NC. In addition, ASITS could be used as a leading predictor of haze events since correlation coefficients of ASITS leading PM2.5 concentrations by 1 day were significant and were larger than simultaneous correlation coefficients in almost all areas in NC. The robust relationship between ASITS and PM2.5 concentrations exists possibly because the index can reflect the activities of synoptic systems. ASITS could be a useful statistical indicator for variations in PM2.5 concentrations and haze events, and a good tool in analyzing the relationship between climate change and long-term variations in haze in NC.

Abstract. Aerosol-cloud interactions (ACIs) have been widely recognized as a factor affecting precipitation. However, they have not been considered in the operational National Centers for Environmental Predictions Global Forecast System model. We evaluated the potential impact of neglecting ACI on the operational rainfall forecast using groundbased and satellite observations and model reanalysis. The Climate Prediction Center unified gauge-based precipitation analysis and the Modern-Era Retrospective analysis for Research and Applications Version 2 aerosol reanalysis were used to evaluate the forecast in three countries for the year 2015. The overestimation of light rain (47.84 %) and underestimation of heavier rain (31.83, 52.94, and 65.74 % for moderate rain, heavy rain, and very heavy rain, respectively) from the model are qualitatively consistent with the potential errors arising from not accounting for ACI, although other factors cannot be totally ruled out. The standard deviation of the forecast bias was significantly correlated with aerosol optical depth in Australia, the US, and China. To gain further insight, we chose the province of Fujian in China to pursue a more insightful investigation using a suite of variables from gauge-based observations of precipitation, visibility, water vapor, convective available potential energy (CAPE), and satellite datasets. Similar forecast biases were found: over-forecasted light rain and under-forecasted heavy rain. Long-term analyses revealed an increasing trend in heavy rain in summer and a decreasing trend in light rain in other seasons, accompanied by a decreasing trend in visibility, no trend in water vapor, and a slight increasing trend in summertime CAPE. More aerosols decreased cloud effective radii for cases where the liquid water path was greater than 100 g m −2 . All findings are consistent with the effects of ACI, i.e., where aerosols inhibit the development of shallow liquid clouds and invigorate warm-base mixed-phase clouds (especially in summertime), which in turn affects precipitation. While we cannot establish rigorous causal relations based on the analyses presented in this study, the significant rainfall forecast bias seen in operational weather forecast model simulations warrants consideration in future model improvements.