Abstract
The impact of changing climate on the frequency of daily rainfall extremes in Jakarta, Indonesia, is analysed and quantified. The study used three different models to assess the changes in rainfall characteristics. The first method involves the use of the weather generator LARS-WG to quantify changes between historical and future daily rainfall maxima. The second approach consists of statistically downscaling general circulation model (GCM) output based on historical empirical relationships between GCM output and station rainfall. Lastly, the study employed recent statistically downscaled global gridded rainfall projections to characterize climate change impact rainfall structure. Both annual and seasonal rainfall extremes are studied. The results show significant changes in annual maximum daily rainfall, with an average increase as high as 20% in the 100-year return period daily rainfall. The uncertainty arising from the use of different GCMs was found to be much larger than the uncertainty from the emission scenarios. Furthermore, the annual and wet seasonal analyses exhibit similar behaviors with increased future rainfall, but the dry season is not consistent across the models. The GCM uncertainty is larger in the dry season compared to annual and wet season.
Highlights
The increase in the atmospheric concentrations of greenhouse gases and the resulting global warming is expected to cause significant changes in the precipitation structure [1,2,3,4,5,6]
The future daily rainfall was generated with Long Ashton Research Station-Weather Generator (LARS-WG) and Statistical Downscaling Model (SDSM) at four stations in Jakarta, and the return period (RP) curves were compared against the observed
15 General Circulation Models (GCM) were used for LARS-WG analysis under AR4 SRES A1B and B1 emission scenarios, only one GCM under AR5 Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios was available and used in the SDSM simulations
Summary
The increase in the atmospheric concentrations of greenhouse gases and the resulting global warming is expected to cause significant changes in the precipitation structure (e.g., amount, extremes, and spatial variability) [1,2,3,4,5,6]. Many studies have analysed historical rainfall and future climate projections thereof from General Circulation Models (GCM) for predefined climate scenarios and quantified the changes in precipitation characteristics at global and regional scales [7,8,9,10]. Are capable of providing station-level rainfall for different GCMs and future climate scenarios [12, 18, 19] In addition to these downscaling models, high-resolution global gridded datasets for historical climate are available for hydrological studies. For future climate a very recent 2015 dataset is the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) [20] for precipitation and temperature. The study conducted a comparative analysis employing two models, LARS-WG and SDSM, and the downscaled gridded dataset, NEX-GDDP Both annual and seasonal (wet, December–March, and dry, June–September) extremes are analysed.
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