Woo-Seop Lee

Amplification of ENSO effects on Indian summer monsoon by absorbing aerosols

In this study, we present observational evidence, based on satellite aerosol measurements and MERRA reanalysis data for the period 1979–2011, indicating that absorbing aerosols can have strong influence on seasonal-to-interannual variability of the Indian summer monsoon rainfall, including amplification of ENSO effects. We find a significant correlation between ENSO (El Nino Southern Oscillation) and aerosol loading in April–May, with La Nina (El Nino) conditions favoring increased (decreased) aerosol accumulation over northern India, with maximum aerosol optical depth over the Arabian Sea and Northwestern India, indicative of strong concentration of dust aerosols transported from West Asia and Middle East deserts. Composite analyses based on a normalized aerosol index (NAI) show that high concentration of aerosol over northern India in April–May is associated with increased moisture transport, enhanced dynamically induced warming of the upper troposphere over the Tibetan Plateau, and enhanced rainfall over northern India and the Himalayan foothills during May–June, followed by a subsequent suppressed monsoon rainfall over all India, consistent with the elevated heat pump (EHP) hypothesis (Lau et al. in Clim Dyn 26:855–864, 2006. doi:10.1007/s00382-006-0114-z). Further analyses from sub-sampling of ENSO years, with normal (<1-σ), and abnormal (>1-σ) NAI over northern India respectively show that the EHP may lead to an amplification of the Indian summer monsoon response to ENSO forcing, particularly with respect to the increased rainfall over the Himalayan foothills, and the warming of the upper troposphere over the Tibetan Plateau. Our results suggest that absorbing aerosol, particular desert dusts can strongly modulate ENSO influence, and possibly play important roles as a feedback agent in climate change in Asian monsoon regions.

Effects of particulate matter on respiratory disease and the impact of meteorological factors in Busan, Korea

BACKGROUNDnBoth air pollution and weather impact hospitalization for respiratory diseases. However, few studies have investigated the contribution of weather to hospitalization related to the adverse effects of air pollution. This study analyzed the effects of particulate matter (PM) on daily respiratory-related hospital admissions, taking into account meteorological factors.nnnMETHODSnDaily hospital admissions for respiratory diseases (acute bronchitis, allergic rhinitis, and asthma) between 2007 and 2010 were extracted from the National Health Insurance Corporation, Korea. Patients were divided into three age-based groups (0-15, 16-64, and ≥65 years). PM levels were obtained from 19 monitoring stations in Busan.nnnRESULTSnThe mean number of patients admitted for acute bronchitis, allergic rhinitis, and asthma was 5.8xa0±xa011.9, 4.4xa0±xa06.1, and 3.3xa0±xa03.3, respectively. During that time, the daily mean PM10 and PM2.5 concentrations were 49.6xa0±xa020.5 and 24.2xa0±xa010.9xa0μg/m3, respectively. The mean temperature anomaly was 7.0xa0±xa02.3xa0°C; the relative humidity was 62.0xa0±xa018.0%. Hospital admission rates for respiratory diseases increased with increasing PM and temperature, and with decreasing relative humidity. A multivariate analysis including PM, temperature anomaly, relative humidity, and age showed a significant increase in respiratory-related admissions with increasing PM levels and a decreasing relative humidity. Higher PM2.5 levels had a greater effect on respiratory-related hospital admission than did PM10 levels. Children and the elderly were the most susceptible to hospital admission for respiratory disease.nnnCONCLUSIONSnPM levels and meteorological factors impacted hospitalization for respiratory diseases, especially in children and the elderly. The effect of PM on respiratory diseases increased as the relative humidity decreased.

The status and prospect of seasonal climate prediction of climate over Korea and East Asia: A review

Over the last few decades, there have been startling advances in our understanding of climate system and in modelling techniques. However, the skill of seasonal climate prediction is still not enough to meet the various needs from industrial and public sectors. Therefore, there are tremendous on-going efforts to improve the skill of climate prediction in the seasonal to interannual time scales. Since seasonal to interannual climate variabilities in Korea and East Asia are influenced by many internal and external factors including East Asian monsoon, tropical ocean variability, and other atmospheric low-frequency variabilities, comprehensive understanding of these factors are essential for skillful seasonal climate prediction for Korea and East Asia. Also, there are newly suggested external factors providing additional prediction skill like soil moisture, snow, Arctic sea ice, and stratospheric variability, and techniques to realize skills from underlying potential predictability. In this review paper, we describe current status of seasonal climate prediction and future prospect for improving climate prediction over Korea and East Asia.

Radiative effect of black carbon aerosol on seasonal variation in snow depth in the Northern-Hemisphere

In this research, we studied the effects of black carbon (BC) aerosol radiative forcing on seasonal variation in the Northern Hemisphere (NH) using numerical simulations with the NASA finite-volume General Circulation Model (fvGCM) forced with monthly varying three-dimensional aerosol distributions from the Goddard Ozone Chemistry Aerosol Radiation and Transport Model (GOCART). The results show that atmospheric warming due to black carbon aerosols subsequently warm the atmosphere and land surfaces, especially those over Eurasia. As a result, the snow depth in Eurasia was greatly reduced in late winter and spring, and the reduction in snow cover decreased the surface albedo. Our surface energy balance analysis shows that the surface warming due to aerosol absorption causes early snow melting and further increases surface-atmosphere warming through snow/ice albedo feedback. Therefore, BC aerosol forcing may be an important factor affecting the snow/ice albedo in the NH.

Weekly heat wave death prediction model using zero-inflated regression approach

Driven partly by the shifting climate and growth of vulnerable populations, excess heat-triggered human fatalities are becoming a serious public health risk concern in Korea. This study develops the zero-inflated regression model for predicting the numbers of weekly heat deaths in South Korea. Defining the heat death as the death caused by “exposure to excessive natural heat,” data analyses are performed to examine statistical relationships between the number of heat deaths versus the pertinent temperature-related parameters and the size of the vulnerable population. The weekly mean of daily maximum temperature and the number of consecutive heat wave days with tropical nights are selected as the temperature-related parameters, while the numbers of elderly living alone and those of agricultural workers are selected as the parameters representing the vulnerable population. Using these four regressive parameters, we develop a regression-based model applied for the prediction of the number of heat deaths. Several statistical methods including the Poisson, negative binomial, hurdle, and the zero-inflated models are scrutinized. The results demonstrate that the zero-inflated Poisson regression model is the most appropriate statistical approach, as it addresses the issue of frequent occurrences of zero-valued observations in weekly heat death data. It was evident that a larger number of heat deaths occurred in the period and study region with a correspondingly higher predicted value. In accordance with this statistical performance, we ascertain that the utilized models could be explored by disaster management and public health experts as a scientific contrivance for health risk mitigation and resource allocation more strategically, and for providing the general public with reliable weekly heat wave risk forecasts.