Introduction Observation of spatial patterns on the rainfall response to ENSO and IOD over Indonesia using TRMM Multisatellite Precipitation Analysis (TMPA).

INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 2014 Published online in Wiley Online Library wileyonlinelibrary.com DOI: 10.1002joc.3939 Observation of spatial patterns on the rainfall response to ENSO and IOD over Indonesia using TRMM Multisatellite Precipitation Analysis TMPA Abd. Rahman As-syakur, a,b I Wayan Sandi Adnyana, c Made Sudiana Mahendra, b I Wayan Arthana, d I Nyoman Merit, c I Wayan Kasa, e Ni Wayan Ekayanti, a,f I Wayan Nuarsa b and I Nyoman Sunarta g a Center for Remote Sensing and Ocean Science CReSOS, Udayana University, Bali, Indonesia b Environmental Research Center PPLH, Udayana University, Bali, Indonesia c Faculty of Agriculture, Udayana University, Bali, Indonesia d Faculty of Oceanography and Fisheries, Udayana University, Bali, Indonesia e Faculty of Science, Udayana University, Bali, Indonesia f Physic Department, Technical High School of Tampaksiring, Bali, Indonesia g Faculty of Tourism, Udayana University, Bali, Indonesia ABSTRACT: Remote sensing data of Tropical Rainfall Measuring Mission TRMM Multisatellite Precipitation Analysis for 13 years have been used to observe the spatial patterns relationship of rainfall with El Ni˜no-Southern Oscillation ENSO and Indian Ocean Dipole IOD over Indonesia. Linear correlation was measured to determine the relationship level by the restriction analysis of seasonal and monthly relationship, while the partial correlation technique was utilized to distinguish the impact of one phenomenon from that of the other. Application of remote sensing data can reveal an interaction of spatial-temporal relationship of rainfall with ENSO and IOD between land and sea. In general, the temporal patterns relationship of rainfall with ENSO confirmed fairly similar temporal patterns between rainfall with IOD, which is high response during JJA June–July–August and SON September–October–November and unclear response during DJF December–January–February and MAM March–April–May. Spatial patterns relationship of both phenomena with rainfall is high in the southeastern part of Sumatra Island and Java Island during JJA and SON. During the SON season, IOD has a higher relationship level than ENSO in this part. In the spatial-temporal pattern seen, a dynamic movement of the relationship between IOD and ENSO with rainfall in Indonesia is indicated, where the influence of ENSO and IOD started during JJA especially in July in the southwest of Indonesia and ended in the DJF period especially in January in the northeast of Indonesia. KEY WORDS rainfall; remote sensing; ENSO; IOD; TMPA; partial correlation Received 23 March 2011; Revised 29 November 2013; Accepted 7 January 2014

1. Introduction

Indonesia is an equator-crossed country surrounded by two oceans and two continents. This location makes Indonesia a region of confluence of the Hadley cell circulation and the Walker circulation, two circulations that greatly affect the diversity of rainfall in Indonesia Aldrian et al., 2007. The annual movement of the sun from 23.5 ◦ N to 23.5 ◦ S produces the monsoon activity, and it also plays a role in influencing the diversity of the rainfall. Local influence of rainfall variability also cannot be ignored because Indonesia is an archipelago with a varied topography Haylock and McBride, 2001; Aldrian and Djamil, 2008. On the other hand, complex distribution of land and sea results in significant local Correspondence to: A. R. As-syakur, Center for Remote Sensing and Ocean Science CReSOS, Udayana University, Bali 80232, Indonesia. E-mail: ar.assyakurpplh.unud.ac.id variations in the annual rainfall cycle Chang et al., 2005; Qian, 2008, and affects the rainfall quantities Sobel et al. , 2011; As-syakur et al., 2013. Furthermore, the differential solar heating between different surface types such as between land and sea, or highland and lowland, causes strong local pressure gradients Qian, 2008. These conditions result in sea-breeze convergence over islands and orographic precipitation Qian et al., 2010, causing diurnal cycle of rainfall over islands. Overall, rain is the most important climate element in Indonesia because precipitation varies both with respect to time and space. Therefore, this study about climate in Indonesia focused more on the rain factor. Atmosphere–ocean interactions near Indonesia such as the El Ni˜no-Southern Oscillation ENSO and the Indian Ocean Dipole IOD mode also contribute to the inter- annual climate variations. Both the climate modes are important because of their large environmental and soci- etal impacts, globally and regionally Luo et al., 2010.  2014 Royal Meteorological Society A. RAHMAN AS-SYAKUR et al. Indonesian rainfall is coherent and strongly correlated with ENSO variations in the Pacific basin Ropelewski and Halpert, 1987; Nicholls, 1988; Aldrian and Susanto, 2003; Hendon, 2003; Aldrian et al., 2007; Hamada et al., 2012 and also correlated with IOD events Saji et al., 1999; Saji and Yamagata, 2003b; Bannu et al., 2005. ENSO is a recurring pattern of climate variability in the eastern equatorial Pacific, which is characterized by anomalies in both sea surface temperature SST; referred to as El Ni˜no and La Ni˜na for warming and cooling periods, respectively and sea-level pressure Southern Oscillation; Philander, 1990; Trenberth, 1997; Naylor et al. , 2001; Meyers et al., 2007. An IOD event refers to strong zonal SST gradients in the equatorial Indian Ocean regions, phase-locked to the boreal summer and autumn Saji et al., 1999; Saji and Yamagata, 2003a, 2003b. Moreover, Indonesian rainfall is also influenced by intra-seasonal 30–90 days Madden–Julian oscillation MJO; e.g. Madden and Julian, 1971, a dominant component of intra-seasonal variability over the Trop- ics Wheeler and Hendon, 2004. Large-scale tropi- cal convection– circulation phase of MJO significantly affects the rainfall variability over Indonesia Hidayat and Kizu, 2010; Oh et al., 2012. Extreme high and low precipitation events were associated with the MJO phase and causes floods and droughts in some places of Indone- sia. However, spatial characteristics and manifestations of MJO are affected and modified by the ENSO and IOD conditions Hendon et al., 2007; Rao et al., 2007; Tang and Yu, 2008; Waliser et al., 2012. During El Ni˜no, the number of intra-seasonal days decreases and the opposite tends to occur in the La Ni˜na event Pohl and Matthews, 2007. On the other hand, during negative IOD, the MJO propagation is slightly stronger than normal and relatively weak during positive IOD Wilson et al., 2013. El Ni˜no La Ni˜na conditions result in a decrease enhance in rainfall in Indonesia Ropelewski and Halpert, 1987; Philander, 1990; Hendon, 2003; Bannu et al. , 2005; Susilo et al., 2013; on the other hand, pos- itive negative IOD is also decreasing enhancing the rainfall in Indonesia Saji et al., 1999; Saji and Yama- gata, 2003b; Bannu et al., 2005; Aldrian et al., 2007. Decrease in rainfall during El Ni˜no and positive IOD resulted in longer dry season than the normal condi- tions Philander, 1990; Saji et al., 1999; Hamada et al., 2002; Hendon, 2003. On the other hand, the La Ni˜na event creates wet condition during the dry season and also increases rainfall at the beginning of the rainy sea- son Bell et al., 1999, 2000; Hendon, 2003, which cre- ates a high risk of flood Kishore and Subbiah, 2002. Meanwhile, in El Ni˜no and positive IOD-related rain- fall deficits, significantly below-normal rainfall from June through December resulted in extreme drought that con- tributes to large forest and peat fires, air pollution and haze from biomass burning Bell and Halpert, 1998; Gut- man et al., 2000; Waple and Lawrimore, 2003; Chrastan- sky and Rotstayn, 2012 and causes extreme low stream- flow in basins Sahu et al., 2012; at the same time, El Ni˜no and positive IOD also influence a decline in crop productivity Irawan, 2003; D’Arrigoa and Wilson, 2008. Precipitation displays high space–time variability that requires frequent observations for adequate representa- tion Hong et al., 2010. Previous estimates of tropical precipitation were usually made on the basis of climate prediction models and the occasional inclusion of very sparse surface rain gauges andor relatively few measure- ments from satellite sensors Feidas, 2010. Rain gauge observations yield relatively accurate point measurements of precipitation but suffer from sampling errors in rep- resenting areal means and are not available over most oceanic and unpopulated land areas Xie and Arkin, 1996; Petty and Krajewski, 1996. However, nowadays, rainfall data required for a wide range of scientific applications can be achieved through meteorological satellites Petty, 1995. Meteorological satellites expand the coverage and time span of conventional ground-based rainfall data for a number of applications by which all hydrology and weather forecasting are made Levizzani et al., 2002. Furthermore, combining information from multiple satel- lite sensors as well as gauge observations and numer- ical model outputs yields analyses of global precipita- tion with stable and improved quality Xie et al., 2007. In Indonesia, precipitation is represented as rain gauge data throughout the country. However, rain gauges have incomplete coverage over remote and undeveloped land areas and particularly over the sea, where such instru- ments are virtually not available. Data that have better spatial-temporal resolutions of rainfall will allow a more quantitative understanding of the causal links of Indone- sian rainfall to larger scale climate features. The use of remote sensing, which has better spatial and temporal resolution data, in a study about rainfall and its spatial relationship to ENSO and IOD in Indonesia, thus offers an exciting opportunity. Tropical Rainfall Measuring Mission TRMM Multi- satellite Precipitation Analysis TMPA products, often called combined or merged analyses, have been utilized in a wide range of applications, including weatherclimate monitoring, climate analysis, numerical model verifica- tions, and hydrological studies Xie et al., 2007. The TMPA rain products are based on TRMM Precipitation Radar PR and TRMM Microwave Imager TMI com- bined rain rates to calibrate rain estimates from other microwave and infrared IR measurements Huffman et al. , 2007. The TMPA product is more suitable for this study because the available rain gauge measurements are also used in the calibration process Mehta and Yang, 2008. For many years, other groups have studied differ- ent locations to validate TRMM 3B43 data. For example, Semire et al. 2012 validated TRMM 3B43 rainfall for Malaysia, As-syakur et al. 2011 compared the TMPA with rain gauge data in Bali, Fleming et al. 2011 eval- uated the TRMM 3B43 using gridded rain-gauge data over Australia, Chokngamwong and Chiu 2008 com- pared the TMPA with rain gauge data in Thailand, and Islam and Uyeda 2007 validated TRMM 3B43 and  2014 Royal Meteorological Society Int. J. Climatol. 2014 RAINFALL RESPONSE TO ENSO AND IOD OVER INDONESIA Figure 1. Spatial cover of research. determined the climatic characteristics of rainfall over Bangladesh. On the other hand, Vernimmen et al. 2012 and Prasetia et al. 2013 have validated for other types of TRMM in Indonesia. Vernimmen et al. 2012 compared and used real-time TRMM 3B42 to monitor drought in Indonesia, and Prasetia et al. 2013 validated TRMM PR over Indonesia. The results have underscored the superi- ority of the TMPA or TRMM 3B43 product and the goal of the algorithm has been achieved to a large extent. Nonetheless, the satellite data display a few drawbacks for tropical regions as bias error against ground base in situ observation. Limitations of the TRMM are that the data are affected by several factors such as causes of non- Sun-synchronous satellite orbit, narrow swath of satellite data, rainfall types convective and stratiform, and insuf- ficient sampling time intervals, which result in loss of information about rainfall values Fleming et al., 2011; Vernimmen et al., 2012; As-syakur et al., 2013; Prasetia et al. , 2013. Based on these conditions, in this work we attempt to use the rainfall data from TMPA products to know the spatial patterns relationship of rainfall with ENSO and IOD over Indonesia. Previous studies on the relationship between rainfall with ENSO and IOD in Indonesia were carried out in many locations that have rain gauge data or model utilizations where the data come only from the rain gauge e.g. Ropelewski and Halpert, 1987; Nicholls, 1988; Hamada et al., 2002; Hendon, 2003; Saji and Yamagata, 2003b; Aldrian et al., 2007. So with the existence of satellite data that have a better spatial- temporal resolution, useful information about the spatial patterns relationship between rainfalls with both types of index can be expected. In this study, the ENSO condition is defined by the Southern Oscillation Index SOI; Ropelewski and Jones, 1987; Ropelewski and Halpert, 1989, 1996; K¨onnen et al., 1998; Hamada et al., 2002, and the IOD condition is defined by Dipole Mode Index DMI values Saji et al., 1999; Saji and Yamagata, 2003a, 2003b. Main analyses are carried out by using seasonal and monthly spatial correlations.

2. Data and Methods