The Uncharacteristic Occurrence of the June 2013 Biomass-Burning Haze Event in Southeast Asia: Effects of the Madden-Julian Oscillation and Tropical Cyclone Activity

One of the worst haze events to ever hit Peninsular Malaysia occurred in June 2013 due to smoke from Riau, Central Sumatra. While biomass-burning in the region is common, the early occurrence of a haze episode of this magnitude was uncharacteristic of the seasonality of extreme fire events, which usually occur between August and October in the Maritime Continent (MC). This study aims to investigate the phenomenology of the June 2013 haze event and its underlying meteorological forcing agents. The aerosol and meteorological environment during the event is examined using the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire hotspot detections and aerosol optical thickness retrievals, satellite-based precipitation retrievals, and meteorological indices. These datasets are then supported by a WRF-Chem simulation to provide a comprehensive picture of the event’s meteorology and aerosol transport phenomenology. While extreme fire events are more characteristic of El Nino years, the MODIS fire count over the MC in June for the years 2001–2015 was highest in 2013 when neutral El Nino/Southern Oscillation (ENSO) conditions prevailed. Although, the mean daily precipitation for June 2013 was below average for June 2003–2015. An early active tropical cyclone (TC) season occurred in 2013, and results show that the combined induced subsidence and flow enhancement due to TC Bebinca and the dry phases of a strong Madden–Julian Oscillation (MJO) event contributed to the event intensification. Results also show that Bebinca induced a decrease in surface relative humidity of at least 10% over Riau, where fire hotspots were concentrated.

Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California

Accurate and reliable reservoir inflow forecast is instrumental to the efficient operation of the hydroelectric power systems. It has been discovered that natural and anthropogenic aerosols have a great influence on meteorological variables such as temperature, snow water equivalent, and precipitation, which in turn impact the reservoir inflow. Therefore, it is imperative for us to quantify the impact of aerosols on reservoir inflow and to incorporate the aerosol models into future reservoir inflow forecasting models. In this paper, a comprehensive framework was developed to quantify the impact of aerosols on reservoir inflow by integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem) and a dynamic regression model. The statistical dynamic regression model produces forecasts for reservoir inflow based on the meteorological output variables from the WRF-Chem model. The case study was performed on the Florence Lake and Lake Thomas Alva Edison of the Big Creek Hydroelectric Project in the San Joaquin Region. The simulation results show that the presence of aerosols results in a significant reduction of annual reservoir inflow by 4-14%. In the summer, aerosols reduce precipitation, snow water equivalent, and snowmelt that leads to a reduction in inflow by 11-26%. In the spring, aerosols increase temperature and snowmelt which leads to an increase in inflow by 0.6-2%. Aerosols significantly reduce the amount of inflow in the summer when the marginal value of water is extremely high and slightly increase the inflow in the spring when the run-off risk is high. In summary, the presence of aerosols is detrimental to the optimal utilization of hydroelectric power systems.