Abstract

A systematic ensemble high resolution climate modelling study over Vietnam has been performed using the PRECIS model developed by the Hadley Center in UK. A 5 member subset of the 17-member Perturbed Physics Ensembles (PPE) of the Quantifying Uncertainty in Model Predictions (QUMP) project were simulated and analyzed. The PRECIS model simulations were conducted at a horizontal resolution of 25km for the baseline period 1961–1990 and a future climate period 2061–2090 under scenario A1B. The results of model simulations show that the model was able to reproduce the mean state of climate over Vietnam when compared to observations. The annual cycles and seasonal averages of precipitation over different sub-regions of Vietnam show the ability of the model in also reproducing the observed peak and magnitude of monthly rainfall. The climate extremes of precipitation were also fairly well captured. Projections of future climate show both increases and decreases in the mean climate over different regions of Vietnam. The analyses of future extreme rainfall using the STARDEX precipitation indices show an increase in 90th percentile precipitation (P90p) over the northern provinces (15–25%) and central highland (5–10%) and over southern Vietnam (up to 5%). The total number of wet days (Prcp) indicates a decrease of about 5–10% all over Vietnam. Consequently, an increase in the wet day rainfall intensity (SDII), is likely inferring that the projected rainfall would be much more severe and intense which have the potential to cause flooding in some regions. Risks due to extreme drought also exist in other regions where the number of wet days decreases. In addition, the maximum 5day consecutive rainfall (R5d) increases by 20–25% over northern Vietnam but decreases in a similar range over the central and southern Vietnam.These results have strong implications for the management water resources, agriculture, bio diversity and economy and serve as some useful findings to be considered by the policy makers within a wider range of climate uncertainties.

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