Tropical modulation of East Asia air pollution

Understanding air pollution in East Asia is of great importance given its high population density and serious air pollution problems during winter. Here, we show that the day-to-day variability of East Asia air pollution, during the recent 21-year winters, is remotely influenced by the Madden–Julian Oscillation (MJO), a dominant mode of subseasonal variability in the tropics. In particular, the concentration of particulate matter with aerodynamic diameter less than 10 micron (PM₁₀) becomes significantly high when the tropical convections are suppressed over the Indian Ocean (MJO phase 5–6), and becomes significantly low when those convections are enhanced (MJO phase 1–2). The station-averaged PM₁₀ difference between these two MJO phases reaches up to 15% of daily PM₁₀ variability, indicating that MJO is partly responsible for wintertime PM₁₀ variability in East Asia. This finding helps to better understanding the wintertime PM₁₀ variability in East Asia and monitoring high PM₁₀ days.

In this study, it is suggested that the daily PM₁₀ level in East Asia is remotely controlled by the convection over the equatorial Indian Ocean and western Pacific. This tropical modulation explains up to 15% of daily PM₁₀ variability in the region.

Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop and compare the performances of different hybridised deep learning approaches with a convolutional neural network and long short-term memory referred to as CLSTM to forecast the daily UVI of Perth station, Western Australia. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is incorporated coupled with four feature selection algorithms (i.e., genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DEV)) to understand the diverse combinations of the predictor variables acquired from three distinct datasets (i.e., satellite data, ground-based SILO data, and synoptic mode climate indices). The CEEMDAN-CLSTM model coupled with GA appeared to be an accurate forecasting system in capturing the UVI. Compared to the counterpart benchmark models, the results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CEEMDAN-CLSTM model in apprehending the complex and non-linear relationships between predictor variables and the daily UVI. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

Tropical mid-tropospheric CO2 variability driven by the Madden-Julian oscillation

Carbon dioxide (CO(2)) is the most important anthropogenic greenhouse gas in the present-day climate. Most of the community focuses on its long-term (decadal to centennial) behaviors that are relevant to climate change, but there are relatively few discussions of its higher-frequency forms of variability, and none regarding its subseasonal distribution. In this work, we report a large-scale intraseasonal variation in the Atmospheric Infrared Sounder CO(2) data in the global tropical region associated with the Madden-Julian oscillation (MJO). The peak-to-peak amplitude of the composite MJO modulation is ∼1 ppmv, with a standard error of the composite mean < 0.1 ppmv. The correlation structure between CO(2) and rainfall and vertical velocity indicate positive (negative) anomalies in CO(2) arise due to upward (downward) large-scale vertical motions in the lower troposphere associated with the MJO. These findings can help elucidate how faster processes can organize, transport, and mix CO(2) and provide a robustness test for coupled carbon-climate models.