Search

Rainfall in Vietnam is strongly influenced by climate drivers which are caused by anomalies in sea surface temperature (SST), sea level pressure (SLP) or geopotential height in the Pacific and the Indian Ocean. This paper analyses the rainfall records at different sites in various regions of Vietnam to determine the influence of the three major climate drivers, the ENSO (El Niño–Southern Oscillation), IPO (Interdecadal Pacific Oscillation) and IOD (Indian Ocean Dipole) and their interaction on the rainfall at each of these sites. A statistical technique called Bayesian model averaging (BMA) is used to discuss and address the uncertainties problem in model and variable selection when fitting observed rainfall data with climate drivers' indices. The results show that the ENSO, IPO and IOD and their interaction overall have very minor roles in influencing the rainfall in Northern Vietnam but they have some influences on the rainfall in the central and southern parts of Vietnam. Furthermore, seasonality analysis shows the spring rainfall in Vietnam is strongly influenced by the ENSO, IOD and ENSO*IOD interaction, with ENSO*IOD interaction strongest in the north. The situation in summer is similar but the IOD is most dominant compared to the ENSO and ENSO*IOD interaction. In autumn, the IOD and ENSO influences on rainfall are weaker while the IPO has strong influence in the Central and Southern Vietnam and no climate driver has any influence in the north. And during winter, the IPO, ENSO and IPO*ENSO interaction are associated with rainfall variability across Vietnam while the IOD influence is insignificant. The results also confirm the previous studies on the teleconnection roles of SST in the Pacific and Indian Oceans on rainfall variability in Vietnam. Influence scale of climate drivers on spring rainfall at various sites in Vietnam. [ABSTRACT FROM AUTHOR]

A comprehensive evaluation of the performance of the coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) for the New South Wales Greater Metropolitan Region (NSW GMR) was conducted based on modelling results for two periods coinciding with measurement campaigns undertaken during the Sydney Particle Study (SPS), namely the summer in 2011 (SPS1) and the autumn in 2012 (SPS2). The model performance was evaluated for fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2) against air quality data from the NSW Government's air quality monitoring network, and PM2.5 components were compared with speciated PM measurements from the Sydney Particle Study's Westmead sampling site. The model tends to overpredict PM2.5 with normalised mean bias (NMB) less than 20%, however, moderate underpredictions of the daily peak are found on high PM2.5 days. The PM2.5 predictions at all sites comply with performance criteria for mean fractional bias (MFB) of ±60%, but only PM2.5 predictions at Earlwood further comply with the performance goal for MFB of ±30% during both periods. The model generally captures the diurnal variations in ozone with a slight underestimation. The model also tends to underpredict daily maximum hourly ozone. Ozone predictions across regions in SPS1, as well as in Sydney East, Sydney Northwest and Illawarra regions in SPS2 comply with the benchmark of MFB of ±15%, however, none of the regions comply with the benchmark for mean fractional error (MFE) of 35%. The model reproduces the diurnal variations and magnitudes of NO2 well, with a slightly underestimating tendency across the regions. The MFE and normalised mean error (NME) for NO2 predictions fall well within the ranges inferred from other studies. Model results are within a factor of two of measured averages for sulphate, nitrate, sodium and organic matter, with elemental carbon, chloride, magnesium and ammonium being underpredicted. The overall performance of CCAM-CTM modelling system for the NSW GMR is comparable to similar model predictions by other regional airshed models documented in the literature. The performance of the modelling system is found to be variable according to benchmark criteria and depend on the location of the sites, as well as the time of the year. The benchmarking of CCAM-CTM modelling system supports the application of this model for air quality impact assessment and policy scenario modelling to inform air quality management in NSW. [ABSTRACT FROM AUTHOR]

Ozone and fine particles (PM2.5) are the two main air pollutants of concern in the New South Wales Greater Metropolitan Region (NSW GMR) due to their contribution to poor air quality days in the region. This paper focuses on source contributions to ambient ozone concentrations for different parts of the NSW GMR, based on source emissions across the greater Sydney region. The observation-based Integrated Empirical Rate model (IER) was applied to delineate the different regions within the GMR based on the photochemical smog profile of each region. Ozone source contribution was then modelled using the CCAM-CTM (Cubic Conformal Atmospheric model-Chemical Transport model) modelling system and the latest air emission inventory for the greater Sydney region. Source contributions to ozone varied between regions, and also varied depending on the air quality metric applied (e.g., average or maximum ozone). Biogenic volatile organic compound (VOC) emissions were found to contribute significantly to median and maximum ozone concentration in North West Sydney during summer. After commercial and domestic sources, power generation was found to be the next largest anthropogenic source of maximum ozone concentrations in North West Sydney. However, in South West Sydney, beside commercial and domestic sources, on-road vehicles were predicted to be the most significant contributor to maximum ozone levels, followed by biogenic sources and power stations. The results provide information that policy makers can use to devise various options to control ozone levels in different parts of the NSW Greater Metropolitan Region. [ABSTRACT FROM AUTHOR]

A crucial aspect of regional air quality modelling is the accurate characterisation of local meteorology, as this plays a significant role in the transport and chemical transformation of pollution across a region. This study explores the potential of The Weather Research and Forecasting (WRF) model as an alternative meteorological model to support near-time air quality predictions for the NSW Greater Metropolitan Region (NSW GMR). We develop an optimal WRF configuration for the NSW GMR by downscaling the latest Bureau of Meteorology (BOM) Australian Community Climate and Earth-System Simulator (ACCESS) Numerical Weather Prediction (NWP) data using a three nested domain setup with resolutions of 12km, 4km and 1 km by varying physical parameterisations including two cumulus schemes, two planetary boundary layer schemes, two microphysical schemes, two compatible shortwave and longwave radiation schemes and a standard land surface model. Thus, a set of 16 experiments were executed and evaluated on different days involving high ozone days, bushfire smoke, dust storms and heatwaves. Each simulation was initialised at 12UTC with a forecast horizon of four days, including three hourly updates of boundary conditions. All simulations were evaluated with metrics including Mean Absolute Errors (MAE) and Index of Agreement (IOA) calculated over selected stations operated by NSW Department of Planning, Industry and Environment (NSW DPIE) and BOM for surface and vertical variables (temperature (T), relative humidity (RH), wind speed (WS)) including precipitation (PRECIP) and planetary boundary layer heights (PBLH). Overall, results showed that the best configuration produced acceptable agreements at the surface for T (IOA > 0.8), RH (IOA > 0.7) and WSP (IOA > 0.6). Errors in PBLH were elevated in the mid-boundary layer but was within 100m where vertical profiles of temperature and winds were well simulated. Similarly, experiments with double moment microphysical schemes were better in simulating precipitation. Other performance metrics for the experiments conducted under different case days will also be discussed. [ABSTRACT FROM AUTHOR]

The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government's Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s−1 mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons. [ABSTRACT FROM AUTHOR]