Validation of the WRF regional climate model over the subregions of Southeast Asia: climatology and interannual variability

Abstract

This study investigates the capability of a regional climate model in simulating the climate variability over Southeast Asia (SE Asia). The present-day climate, covering the period 1991 to 2015, was dynamically downscaled using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 27 km. The initial and boundary conditions for the WRF model is provided with the European Centre for medium-range weather forecasting (ECMWF) reanalysis (ERA-Interim) data. The model reproduced the mean precipitation climatology as well as the annual cycle. Nevertheless, the model overestimated the boreal summer precipitation over the SE Asian mainland, and underestimated the boreal winter precipitation over the Indonesian region. Model biases are associated with the bias in simulating the vertically integrated moisture fluxes. At an interannual scale, the model shows good performance over the SE Asian mainland and the Philippines in all seasons except for the boreal summer. The influence of El Niño/Southern Oscillation (ENSO) on rainfall over mainland SE Asia and the Philippines during JJA is weak, and the model successfully simulated the weak relationship realistically. In contrast, model interannual variability over the Indonesia region is good only in boreal summer and autumn seasons. This is because the model successfully simulated the significant negative correlation between rainfall and ENSO. The influence of the Indian Ocean Dipole (IOD) is seen only in the boreal autumn over the Indonesian region, and the model reproduced it reasonably well. The improvement in the representation of precipitation anomaly associated with ENSO/IOD is due to reasonably accurate simulation of large-scale circulation over SE Asia

Differential Influences of Teleconnections from the Indian and Pacific Oceans on Rainfall Variability in Southeast Asia

“…An apparent eastward/northeastward movement of the negative loading at the first two seasons of JJA and SON was also shown in the IOD event after removing the El Niño signal (Figure 2e,f). The features of the pure El Niño and IOD events during the SON season are also consistent with Saji and Yamagata [13] and Ratna et al [63], where the El Niño and IOD events had the same impacts on Indonesia, leading to an atmospheric anomaly and subsequently to a severe drought over the region. Hamada et al [14] also notes that the IOD event caused drier conditions in the western Java Sea during this season, which is consistent with Figure 2f.…”

“…The easterly wind at the south of the equator, which is also part of the two anti-cyclonic systems, transported away the moisture from the area at the south of the equator to the Indian Ocean, while the northern Indochina region received moisture from the Bay of Bengal (Figures 1j, 5j and 6j). Ratna et al [63] also agreed that there is strong divergence over SEA that causes a negative rainfall anomaly during the positive IOD events which is modulated by the strong easterly winds over the southwestern region of Indonesia. The divergence over the Indonesia region is due to the IOD-induced cold SST anomaly and converges over the western Indian Ocean due to a warm SST anomaly there.…”

“…During JJA, the southern part of SEA experienced drier conditions, and in the following season the dry condition covered the eastern and southern area of SEA (Figure 1i,j). According to Ratna et al [63], rainfall tends to decrease over the northwestern Java, especially during dry season (August-October). Drought condition will occur when the IOD event co-occurs with the El Niño event.…”

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

“…Kajian tentang prediksi iklim musiman telah banyak dilakukan di Indonesia. Beberapa prediktor yang sering digunakan dalam prediksi iklim musiman antara lain suhu permukaan laut (Chen et al, 2016) wilayah Indonesia, Dipole Mode Index (Ratna et al, 2017) dan ENSO (Yanto et al, 2016) dengan time lag yang beragam Wang et al, 2015). Penelitian terdahulu umumnya menggunakan hubungan berbasis time lag antara prediktor dan prediktan dalam mengembangkan model prediksi.…”

Developing Parametric Insurance for Weather Related Risks for Indonesia

“…CRU uses the Angular Distance Weighted (ADW) technique to interpolate precipitation grid cell from the eight nearest weather stations 57 . CRU data have been validated over Southeast Asia and recently the data were used for validation of the regional climate model WRF over Southeast Asia 58 and compared to station based gauge products in Vietnam 59 and attribution of climate change 60 . The CRU dataset is one of the most extensively used data by the climate modelling community and has been used by the National Weather Service of Indonesia (BMKG).…”