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We present preliminary analyses of the historical (1986-2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models’ performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better. [ABSTRACT FROM AUTHOR]

• The model bias is decomposed into the ensemble bias and the inter-model modes. • The CMIP6 models simulate well the meridional march and the annual climatology. • The long-term trends are poorly represented in CMIP6 models. • The CMIP6 models can only reproduce about 23% of interannual variability. • The best 3 models are EC-Earth3, EC-Earth3-Veg and E3SM-1-0. This study evaluated the bias in rainfall over Southeast Asia in the historical simulations of the sixth phase of Coupled Model Intercomparison Project (CMIP6). Using 48 CMIP6 models, a suite of evaluation methods was applied to examine in detail – annual meridional march, annual climatology, long-term trend, and interannual variability. For this study, multi-source observational datasets were employed for comparisons against model simulations. The biases in the climatological state were decomposed into ensemble biases and the leading modes of inter-model uncertainty. The CMIP6 models could simulate well the climatological state of meridional march and annual mean with high pattern correlation coefficients of 0.88 and 0.59, respectively. However, the performance is poor in simulating the long-term trend that exhibits a correlation coefficient of 0.05 between simulation and observation in spatial patterns. The CMIP6 models could only reproduce about 23% of interannual variability. This evaluation not only revealed the systematic biases in 48 CMIP6 models but also illustrated the biases in the different Empirical Orthogonal Function (EOF) modes of inter-model uncertainty and the relative performance ranking for a particular model. Based on the total performance index, the best 3 models are EC-Earth3, EC-Earth3-Veg, E3SM-1-0 and relatively poor performance were seen in INM-CM4-8, AWI-ESM-1-1-LR and MCM-UA-1-0. The study provides an assessment of the overall uncertainties in CMIP6 models in the historical simulations and could also be a useful feedback to the global climate modellers in optimizing the models for better performance. [ABSTRACT FROM AUTHOR]

Optimal designs of stormwater systems rely very much on the rainfall Intensity-Duration-Frequency (IDF) curves. As climate has shown significant changes in rainfall characteristics in many regions, the adequacy of the existing IDF curves is called for particularly when the rainfall are much more intense. For data sparse sites/regions, developing IDF curves for the future climate is even challenging. The current practice for such regions is, for example, to 'borrow' or 'interpolate' data from regions of climatologically similar characteristics. A novel (3-step) Downscaling-Comparison-Derivation (DCD) approach was presented in the earlier study to derive IDF curves for present climate using the extracted Dynamically Downscaled data an ungauged site, Darmaga Station in Java Island, Indonesia and the approach works extremely well. In this study, a well validated (3-step) DCD approach was applied to develop present-day IDF curves at stations with short or no rainfall record. This paper presents a new approach in which data are extracted from a high spatial resolution Regional Climate Model (RCM; 30 × 30 km over the study domain) driven by Reanalysis data. A site in Java, Indonesia, is selected to demonstrate the application of this approach. Extremes from projected rainfall (6-hourly results; ERA40 Reanalysis) are first used to derive IDF curves for three sites (meteorological stations) where IDF curves exist; biases observed resulting from these sites are captured and serve as very useful information in the derivation of present-day IDF curves for sites with short or no rainfall record. The final product of the present-day climate-derived IDF curves fall within a specific range, +38% to +45%. This range allows designers to decide on a value within the lower and upper bounds, normally subjected to engineering, economic, social and environmental concerns. Deriving future IDF curves for Stations with existing IDF curves and ungauged sites with simulation data from RCM driven by global climate model (GCM ECHAM5) (6-hourly results; A2 emission scenario) have also been presented. The proposed approach can be extended to other emission scenarios so that a bandwidth of uncertainties can be assessed to create appropriate and effective adaptation strategies/measures to address climate change and its impacts. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]