Akhmad Faqih

Prediksi Awal Musim Hujan di Jawa Menggunakan Data Luaran Regional Climate Model Version 3.1 (RegCM3)

Monsoon onset information plays an important role in setting up planting strategy for achieving optimum yield . This study aimed to develop forecasting model for the monsoon onset in main rice growing area of Java used Regional Climate Model Version 3.1 (RegCM3). The forecasting models of the monsoon onset and September-Oktober-November (SON) rainfall data were developed using regression model that have the highest coefficient determination and the models were tested using likelihood ratio test. It was found that the forecasting models of the monsoon onset and September-Oktober-November rainfall data were polynomial orde 2 or cuadratic that have coefficient determination 69%, 74%, 80% and 86%. Likelihood ratio test found that RegCM3 rainfall data was not significantly different with observation rainfall data ( α = 0.05) . Onset in Java between 25 th until 34 th of 10-days period (early September until early December).

North American Multi Model Ensemble (NMME) Performance of Monthly Precipitation Forecast over South Sulawesi, Indonesia

The North American Multi-Model Ensemble (NMME) as one of the multi-model seasonal forecasting system, regularly generate monthly precipitation forecast for all globe with 0.5 – 11.5 months lead time. This useful information can be used as general input to regional and local precipitation forecast. This study quantifies monthly precipitation hindcast data performance in South Sulawesi provided by seven coupled models from the NMME during 29 years (1982 – 2010) period. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset and the Standardized Verification System (SVS) for long-range forecasts (LRF) analysis applied to asses monthly precipitation prediction. Almost all individual model shows relatively high skill when used to make June – November monthly precipitation prediction and low skill when used to forecast monthly precipitation in December – May periods. Multi-Model Ensemble (MME) performed using Simple Composite Method (SCM) increased performance of monthly precipitation prediction. Nevertheless, this improvement only applies at short lead time (< 3.3 months). This result also shows that NMME is more promising when it used to make precipitation forecast application during dry period (i.e. drought prediction) rather than wet period over South Sulawesi region.

Observed and blended gauge-satellite precipitation estimates perspective on meteorological drought intensity over South Sulawesi, Indonesia

South Sulawesi province as one of the rice production center for national food security are highly influenced by climate phenomenon that lead to drought condition. This paper quantifies meteorological drought based on Standardized Precipitation Index (SPI) recommended by the World Meteorological Organization (WMO) and Consecutive Dry Days (CDD) as one of the extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices were calculated by using (i) quality controlled daily and monthly observational precipitation data from 23 weather stations of various record lengths within 1967-2015 periods, and (ii) 0.05o x 0.05o blended gauge-satellite of daily and monthly precipitation estimates of the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset. Meteorological drought intensity represented by Average Duration of Drought Intensity (ADI) from three-monthly SPI (SPI3) show spatial differences characteristic between eastern and western region. Observed and CHIRPS have relatively similar perspective on meteorological drought intensity over South Sulawesi. Relatively high values of ADI and longest CDD observed mainly over south western part of study area.

Climate Changes Projection for Land and Forest Fire Risk Assessment in West Kalimantan

Risk analysis is a method used to determine the probability of disaster in the current and future. This research analyse of fire risk in West Kalimantan by using extreme climate and vulnerability analysis. Extreme climate was calculated based on the extreme dry rainfall from regional climate model RegCM4.4 outputs. Vulnerability analysis was conducted by using a composite mapping analysis used hotspot data and eleven indicators of vulnerability. We found that very high level of extreme dry rainfall located in the southern region and the western coast area of West Kalimantan. This condition was influenced by environment factors such as topography and land use. Extreme dry rainfall also associated with the pattern of annual rainfall in West Kalimantan which ranges between 1753-4861 mm. Modelling of the vulnerability of land and forest fires in West Kalimantan showed that the land use has impact 24% on the vulnerability model. The results of the vulnerability model analysis shows that the plantations areas and secondary swamp forests are highly vulnerable, particularly on the peat area with depth about 50-200 cm. The analysis of land and forest fires risk found that the vulnerable areas have high risk which is largely unmanaged plantation areas and peatlands.

Future rainfall variability in Indonesia under different ENSO and IOD composites based on decadal predictions of CMIP5 datasets

El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are amongst important climate drivers that play a significant role in driving rainfall variability in Indonesia, especially on inter-annual timescales. The phenomena are suggested to have an association with interdecadal climate variability through the modulation of their oscillations. This study aims to analyse the characteristics of future rainfall variability in Indonesia during different condition of ENSO and IOD events based on decadal predictions of near-term climate change CMIP5 GCM data outputs up to year 2035. Monthly data of global rainfall data with 5x5 km grid resolutions of CHIRPS dataset is used in this study to represent historical rainfall variability as well to serve as a reference for future rainfall predictions. The current and future rainfall and sea surface temperature data have been bias corrected before performing the analysis. Given the comparison between rainfall composites during El-Nino and positive IOD events, the study showed that the future rainfall conditions in Indonesia will become drier than the historical condition resulted from the same composite approach. In general, this study showed the Indonesian rainfall variability in the future is expected to respond differently to a different combination of ENSO and IOD conditions.