This study uses an ensemble of regional climate models (RCMs) to simulate and project the climate of Vietnam. Outputs of 3 global climate models are dynamically downscaled using 3 RCMs. Experiments are performed for a baseline period from 1980 to 1999 and for a future projection from 2000 to 2050 with the A1B emission scenario. Verifications against observations at 61 selected meteorological stations in the region show that an ensemble mean of the 3 RCMs outperforms the individual RCM in representing the climatological mean state, and reasonably captures some extreme climate indices such as the annual maximum daily temperature (TXx), the annual minimum daily temperature (TNn), and the annual maximum 1 d precipitation (RX1day). Future ensemble projections of the temperature, precipitation, and 3 different extreme indices are then evaluated. The simulations predict the 2 m air temperature over Vietnam to significantly increase in both the near future 2011-2030 and middle-future 2031-2050 periods compared to the baseline period. The temperature trend tends to be positive and significant over the whole Vietnam for the spring, summer and fall periods, whereas it is insignificant in the north central region during winter. The highest increase of ~0.5°C decade-1 appears to be pronounced in summer. For precipitation, future changes vary depending on regions and seasons, with the most significant increasing trend over the coastal plain of Central Vietnam, particularly during the winter monsoon season. Under the global warming scenario A1B, TXx and TNn show a significant increase, with the highest rate in the northern and central highlands regions of Vietnam. The extreme precipitation RX1day indices show increasing trends for the coastal zone in the south central region of Vietnam, suggesting more severe water-related disasters in this region in the future. © Inter-Research 2014.
CITATION STYLE
Ngo-Duc, T., Kieu, C., Thatcher, M., Nguyen-Le, D., & Phan-Van, T. (2014). Climate projections for Vietnam based on regional climate models. Climate Research, 60(3), 199–213. https://doi.org/10.3354/cr01234
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