This study examines the simulated temperature over Southeast Asia (SEA) using the Regional Climate Model version 4.3 (RegCM4.3), and its sensitivity to selected cumulus and ocean surface flux schemes. Model simulations were conducted for the SEA domain at 36 km spatial resolution for the period of 1989-2008, as part of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-Southeast Asia) project. A total of 18 sensitivity experiments were conducted with a combination of six cumulus parameterization schemes and three ocean surface flux schemes. The model's skill in representing mean, maximum and minimum temperatures is evaluated against observed gridded data sets. Results indicate a predominant cold bias in all simulations, particularly over mainland SEA (Indochina) during the season of December to February. Nevertheless, the seasonal correlation is highest over this region. The cold bias of the model is also evident in the temperature distributions, such that there are more cold months than observed, which may be associated with the underestimation of the daily maximum temperature. A few simulations also reveal a warm bias over some areas in the Maritime Continent. Further examination shows that both radiative and surface fluxes influence the simulated temperature, which may also have effects that partially offset each other in some areas. Comparison of the sensitivity experiments reveals differences in model performance, and underlines the importance in choosing the appropriate configuration for RegCM4.3 before it is used to downscale climate projections, particularly for the SEA region. This study also shows a strong influence of the choice of cumulus scheme on temperature. Based on performance metrics for temperature among the schemes tested, the Massachusetts Institute of Technology (MIT) Emanuel cumulus scheme and the Biosphere-Atmosphere Transfer Scheme version 1e (BATS1e) ocean surface flux scheme can be used in future simulations for the region.

Japan Meteorology Agency (JMA) developed SATAID (Satellite Animation and Interactive Diagnosis) application to display and to retrieve some meteorology parameter values in satellite image data. This paper studies the use of the application in analyzing the Jakarta flood February 1st, 2008 and the Yogyakarta Tropical cyclone February 18th, 2007. The procedure described in this paper can be applied in another issues as a reference material in analyzing SATAID image data.

Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature‐forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity‐related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low‐ and high‐resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere‐only runs) and Hist‐1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high‐resolution HighResMIP model compared to the low‐resolution HighResMIP model.

Global Climate Models (GCMs) have been the primary source of information for constructing climate scenarios, and they provide the basis for climate change impacts assessments of climate change for a range of scales, from global down to regional scale. Due to the coarse spatial resolution, the GCM outputs have to be downscaled to resolve the scale discrepancy between the resolutions required for impact assessments and the model’s resolution. However, it is important to bias-correct (BC) the raw climate projection outputs which ideally correct the discrepancy between a model’s climate and the observed historical climate. In this study the results of bias correction of daily precipitation over the Indonesian region from downscaled CMIP5 GCM climate simulations using an optimized configuration of the Regional Climate Model (RegCM) for a baseline period of 16 years (1990–2005) with respect to observation is discussed in detail. The statistical bias correction method validated in this study is based on the initial assumption that both observed and simulated intensity distributions are well approximated by the Gamma distribution and the correction is made by matching the quantiles of the Gamma cumulative distribution functions. Overall, the results suggest that when the bias-correction is applied on dynamically downscaled model, it improved the skill in simulating the precipitation over Indonesia and this is a useful tool for further regional downscaling studies.

The Paris Agreement, signed in 2015, is intended to limit global warming well below 2°C. This paper is aimed to assess the potential key impact of 2°C and 4°C global warming on the characteristics of precipitation extremes over Indonesia. For this purpose, the CSIRO-Mk3.6.0 global projections forced by the representative concentration pathway (RCP8.5) scenario is dynamically downscaled using the RegCM modelling system. The results show that under these two global warming level, total annual precipitation (PRCPTOT) will decrease in most regions. Consistently, the dry spell duration (CDD, consecutive dry days) is projected to increase. On the other hand, the frequency and the intensity of precipitation extremes (R50mm and RX1day) are projected with mix increase and decrease tendency. Seasonally, the contrast changing of PRCPTOT is projected. PRCPTOT tends to decrease during dry season (June-July-August, JJA) and tends to increase during wet season (December-January-February, DJF). The similar pattern is found for other indices e.g. CDD, R50mm and RX1day. In general, changes at 4°C global warming are statistically more significant and more intensified compared to that at 2°C. Our findings suggest the benefit of limiting global warming at a lower level.