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Extreme Rainfall in Katulampa Associated with the Atmospheric Circulation
Available online at www.sciencedirect.com
ScienceDirect Procedia Environmental Sciences 33 (2016) 155 – 166
The 2nd International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2015, LISAT-FSEM 2015
Extreme rainfall in Katulampa associated with the atmospheric circulation Gigih Bangun Wicaksono*, Rahmat Hidayat Departement of Geophysic and Meteorology, Faculty of Sains and Mathematic, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680, West Java, Indonesia
Abstract Extreme rainfall will increase the river flow and it can be triggered by regional and global atmospheric circulation. Objective of this study is to analyze the intensity of rainfall in Katulampa and its associated with the atmospheric circulation. We found that the extreme rainfall threshold in Katulampa is 67 mm in December-January-February (DJF) which is determined using 95% percentile method. The number of extreme rainfall events is 86 days during 1981 to 2013. The results showed that increasing of rainfall intensity in Katulampa is influenced by the Madden-Julian Oscillation (MJO) and cold surge. MJO and cold surge modify the transport of water vapor and triggering the formation of convective clouds over the Java Island. Phase 2,3,4 and 5 of MJO have the highest impact on increasing rainfall intensity in Katulampa which is characterized by Outgoing Longwave Radiation (190 - 210 W/m2). Meanwhile, the cold surge event has time lag about 1-2 days to increase the rainfall intensity in Katulampa. © 2016 2016The TheAuthors. Authors. Published by Elsevier Published by Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of LISAT-FSEM2015. Peer-review under responsibility of the organizing committee of LISAT-FSEM2015 Keywords: atmospheric circulation; cold surge; extreme rainfall; madden-julian oscillation.
1. Introduction An increasing amount of attention has been given to atmospheric sciences such as the occurrences of weather and climate extremes. The occurrence of extreme events is influenced by climate variations on different time scales, which include intraseasonal oscillations, interannual variabilities and interdecadal changes [1, 2]. Weather and climate extreme events are often occurred in the tropical region including Indonesia. These conditions cause the complex system of atmospheric circulation in Indonesia maritime continent due to the influence of the monsoon. The
* Corresponding author. Tel.: +62-857-1096-0352. E-mail address: [email protected].
1878-0296 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of LISAT-FSEM2015 doi:10.1016/j.proenv.2016.03.066
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occurrence of monsoons are influenced by differential heating between the ocean and land leading to differences in the amount of water vapor. Anomalous of the monsoonal precipitation are affected by water vapor transport. Zhou and Yu [3] stated that the large amounts of water vapor have an impact on rainfall anomalies that occur in the region through the monsoonal winds. Rainfall in Katulampa dam in the west Java is influenced by the Asia-Australia monsoon [4]. The Asian monsoon coincided with cold surge events increase rainfall in the north of Java Island significantly [5]. Wu et al. [6] pointed out that the strong northerly wind events occurred five times over the South China Sea in 2007. This strongest event enhances northerly wind blew across the equator, hence increasing rainfall over Java [6]. Research related to rainfall anomaly in Katulampa dam is needed to be assessed because Katulampa dam has an important role as the gates that keep the water flow from the Ciliwung Hulu watershed. Study of rainfall anomaly in Ciliwung Hulu watershed has done by Komeji [7]. He made a threshold extreme of Katulampa rainfall is 149 mm, which may cause flooding in Jakarta. Flood events in Jakarta cause huge economic losses. Bapennas [8] declared a total loss incurred when the Jakarta flood event in 2007 reached 5.16 trillion rupiah, with the potential economic loss reached 3.60 trillion rupiah. So, the objective of this study is to determine the extreme rainfall and analyzed the extreme rainfall events in Katulampa dam associated with the tropical atmospheric circulation. 2. Methods The data used are the Katulampa daily rainfall data (06º 38'S, 106º 50'E, with elevation of 347 meters above sea level) from BPSDA Ciliwung-Cisadene 1981-2013, Katulampa daily discharge data from the Central of CiliwungCisadane River Region 1992-2013, and five atmospheric parameters data as shown in Table 1 [9, 10]. Table 1. Atmospheric parameters data used for mapping. Resolution Data Type Temporal
Spatial (o)
Zoal wind
Daily
2,5 x 2,5
Meridional Wind
Daily
2,5 x 2,5
Surface pressure
Daily
2,5 x 2,5
Specific humidity
Daily
2,5 x 2,5
Outgoing Longwave Radiation (OLR)
2,5 x 2,5 Daily
Source
iridl.ldeo.columbia.edu/SOURCES /.NOAA/.NCEP-NCAR/.CDAS-1 (Kalnay et al. 1996) iridl.ldeo.columbia.edu/SOURCES /.NOAA/.NCEP/.CPC/.GLOBAL (Liebmann dan Smith 1996)
Following Guan et al. [2], we determine the threshold of extreme rainfall by using the 95% percentile method. The same method was done by the Intergovernmental Panel on Climate Change (IPCC) to determine the threshold of extreme precipitation [11]. The threshold is calculated using equation (1) as proposed by Walpole [12]. ݄ܶ ݈݄݀ݏ݁ݎൌ
ሺ݅ݔሺ݊ ͳሻ ͳͲͲ
(1)
Where i is percentile (1, 2, ..., 99) and n is number of data. Rainfall data must be ordered first from the smallest to the largest value and the value of precipitation <1 mm / day is not included in the calculation of the threshold. The threshold values were then obtained with the 95% percentile method. We also compared the threshold rainfall extreme that given from equation (1) with a threshold calculated by equation (2). ݄ܶ ݈݄݀ݏ݁ݎൌ ܽ ݈݈݂ܽ݊݅ܽݎ݂݁݃ܽݎ݁ݒ ሺʹ ݈݈݂ܽ݊݅ܽݎ݂݊݅ݐܽ݅ݒ݁݀݀ݎܽ݀݊ܽݐݏݔሻ
(2)
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Rainfall exceed the threshold are then categorized as extreme rainfall events. Extreme rainfall events during 1981-2013 will be recorded throughout date as the reference for collecting atmospheric parameters data to be used for composite map to explain the role of atmospheric conditions on the extreme rainfall events. Zonal and meridional wind data used to identify the direction and speed wind and the center of divergence / convergence which are calculated by equation (3) [13].
1 wu 1 wv v tanI a cosI wO a wI a where a is earth radius, I is latitude, λ is longitude, u is zonal wind, v is meridional wind. & & H vH
(3)
This study was confined to the Indonesian region, especially in the range of 200 S - 200 N and 600 E - 1800 E, the latitude angle tangent value is quite small compared to the radius of the earth (a), so that the third term of equation (3) can be ignored. Because a cosI dO = dx (zonal distance) and a dI =dy (meridional distance), then the equation (4) can be written as:
& & H vH
wu wv wx wy
(4)
Specific humidity data is used to estimate the water vapor transport which is calculated by equation (5) [3]. Therefore the outgoing longwave radiation (OLR) data is used to indicate the cloud condition. ଵ
௧
(5)
ܳ ൌ ௦ ܸ݀ݍ
where Q is moisture transport, g is gravitation, q is specific humidity, V is wind vector, dp is pressure changing, ps is surface pressure (1000 hPa), pT is top pressure (300 hPa). 3. Results 3.1. Threshold determination of extreme rainfall Extreme rainfall is rain events exceeding the threshold extreme calculated by equation (1) and (2). The threshold values resulting from the calculation method of 95% percentile and average method + (2 x standard deviation) have the results of each 67 mm and 66.7 mm, respectively (Table 2), so that these values are used for the determination of extreme rainfall. Daily rainfall distribution in 1981-2013 December-January-February (DJF) is depicted in Fig. 1. The rainfall exceeds the threshold extreme rainfall (black line) recorded as of the date of extreme rainfall events are shown in Table 3. Table 2. Threshold and the number of extreme rainfall events years 1981-2013 in Katulampa Method
Percentile 95%
Average + (2 x stdev)
Season
Threshold (mm)
Extreme numbers (days)
Threshold (mm)
Extreme numbers (days)
DJF
67
86
66.7
86
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Fig. 1. DJF rainfall and limits on extreme rainfall events in Katulampa years 1981 to 2013. Table 3. Date of extreme rainfall events on DJF in Katulampa the with 95% percentile method. Year 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
January 30 13 14, 17 12, 13 20 12 21, 30 26 3, 5 3 13, 24 1 25 29, 31 18, 23, 26 18 29 1, 17 20 9 15
February 8, 16 7 4 11, 27 25 12 8, 26 8 10 6 8, 26 3, 4, 7, 13 9 3, 16, 17 12, 17 22 -
December 25, 26 20 22 13, 27 3, 5 19, 24 9, 15 2 7 2 25 11, 19 3, 5, 7, 10 3 23 27 4 24, 27 8, 23 -
It is seen in Fig. 2 that inter-annual variability of frequency of extreme rainfall events in Katulampa. The number of extreme rainfall occurred about 2-3 days in a year. The largest number occurred in 2007 (8 days), while in 1990 did not occur the extreme rain in Katulampa.
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Fig. 2. The frequency of extreme rainfall events years 1981-2013 in Katulampa.
3.2. Atmospheric circulation in extreme rainfall events in Katulampa Rainfall will increase in case of heavy rains and evenly distributed. The amount of rainfall and its distribution is determined by the type of clouds, one of which is the convective clouds. Convective clouds are formed due to removal of air masses are large and focused on an area that is affected by atmospheric circulation (zonal and meridional wind movement).
A
B
Fig. 3. Surface pressure of the average DJF (a) and extreme rainfall events (b) years 1981-2013 in Katulampa.
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Naturally, the wind blows due to the pressure difference is from high pressure to the low pressure. Fig. 3a shows the distribution of regions with high and low pressure when the DJF (Climatology) and during extreme rainfall events observed in Katulampa. Fig. 3b shows the high (low) pressure is observed in the northern (southern) latitudes. These patterns are typically observing in DJF. However, the pressure over the South China Sea is increasing during extreme rainfall event. It implies that the occurrence of cold surge as variations between seasons of monsoon AsiaAustralia often observed in November to March [5]. Fig. 3a and 3b also show the pressure in Australia is lower than the southern Indian Ocean due to the heating in mainland. In addition, Fig. 3b shows the center of the cyclone observed in the Indian Ocean during the extreme rainfall events. Kantha et al. [14] and Yamada et al. [15] stated that frequency of cyclone centered in the Indian Ocean due to the transfer of energy towards the west along with the Asian monsoon, hence the movement of the wind will be focused in the regions.
A
B
Fig. 4. Wind on average DJF (a) and extreme rainfall events (b) years 1981-2013 in Katulampa.
Fig. 4a and 4b show wind direction during DJF season and the extreme rainfall events. The winds blow from the north to south through the South China Sea to the Strait Karimata and from south to north through the Indian Ocean region. The wind speed is increased during extreme rainfall event (Fig. 4b). Hattori et al. [5] argue that the increasing of wind speed is called a cold surge. Cold surge occurs due to the variations in pressure and wind speed over the South China Sea. Two-way movement of the wind brings moisture from the Pacific Ocean and from the South China Sea. Such moisture leads to convergence region and gradually enhancing the convective clouds and heavy rains in Jakarta [6].
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The wind is carrying huge moisture by transporting water vapor. This transport of water vapor is a large scale in the lower troposphere [16]. Fig. 5 shows the transport of water vapor during extreme rainfall events. Fig. 5a and 5b also show the highest water vapor transport in the Java Sea and the Indian Ocean. The Pacific Ocean and the South China Sea act as a source of water vapor. This centralized water vapor will trigger the formation of convective clouds in the cyclonic area.
Fig. 5. Transport of water vapor in extreme rainfall events
Center of convergence region and cloud formation can be identified from the Outgoing Longwave Radiation (OLR). The centers of convergence are shown in blue and red color in Fig. 6a and 6b. During the extreme events convergence center in the Indian Ocean is getting stronger.
A
B
Fig. 6. Divergence and convergence of surface winds on average DJF (a) and extreme rainfall events (b) years 1981-2013 in Katulampa.
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A
B
Fig. 7. Outgoing longwave radiation (OLR) on average DJF (a) and the extreme rainfall events (b) years 1981-2013 in Katulampa.
This convergence patterns show ITCZ happened in the southern hemisphere in DJF (Fig. 6). ITCZ is synonymous with the formation of convective clouds that may be suspected of OLR. OLR values indicate the thickness of the cloud. The smaller the value of OLR indicate clouds increasingly thick. This occurs because the grain of the water contained in the clouds can absorb the energy of long-wave radiation emitted by the earth surface. Fig. 7 shows the OLR in average conditions DJF and extreme events. Both of the images show the OLR around the Sunda Strait to the Java Sea less than 190 W / m2. This value indicates a thick cloud cover in the region that affects an increasing in the intensity of rainfall in Katulampa. However, in the event of extreme convective clouds are formed larger and centered on the Indian Ocean, Indonesian Islands to the Western Pacific Ocean as indicated by OLR values between 190-210 W / m2 (Fig. 7B).
Fig. 8. Outgoing longwave radiation anomalies on the extreme rainfall events in Katulampa.
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OLR anomalies (Fig. 8) obtained from the difference between the values of OLR in extreme rainfall events in Katulampa with an average value of OLR DJF months. OLR is negative indicates a clouds thicker. Visible cloud cover distributed throughout the Indonesian archipelago, but the West Java has a thickness greater than the cloud of other Indonesian areas indicated by anomalous OLR -5 W / m2. The Areas with the most density of cloud cover central Indian Ocean cyclonic happens when extreme rainfall events with OLR anomalies value of -15 W / m2. 3.3. Madden-Julian Oscillation influence on Atmospheric Circulation in extreme rainfall events in Katulampa Madden-Julian Oscillation (MJO) is the intra-seasonal energy transfer system on ocean-atmosphere interactions that have a significant impact on the atmospheric circulation in the Indian Ocean Island States of Indonesia, and the Pacific Ocean to the west. MJO occurrences may result in intra-seasonal variability and instability can strengthen the internal conditions that affect the response of the ocean circulation of the atmosphere above it. The atmospheric circulation will result in increased rainfall in the Indian Ocean region, the West Coast of Sumatra and Indonesia Island States [17].
Fig. 9. Transport of water vapor when the MJO [phase 1 and 8 (A), phases 2 and 3 (B), phase 4 and 5 (C), phases 6 and 7 (D)] and anomalous OLR when MJO [phase 1 and 8 (E), phases 2 and 3 (F), phase 4 and 5 (G), phases 6 and 7 (H)] in the extreme rainfall events in Katulampa.
Hidayat and Kizu [18] have examined the impact of the MJO variability of rainfall in Indonesia with the data of five daily average of rainfall during 1979-1990 from 31 stations throughout Indonesia rain, wind components (zonal,
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meridional, and vertical), and long-wave radiation. Rainfall in Indonesia increased by 1-3 mm / day on the mainland and 5 mm/day in the ocean during MJO events. This difference is due to the topography of the land factor is more varied than the sea, so that local factors more influential than the MJO. Anomalous conditions of water vapor transport and OLR when the MJO in extreme rainfall events in Katulampa are divided into several MJO phases based on the division of occurrence (Fig. 9). According to Wheeler and Hendon [19] MJO is divided into 8 regions, namely phase 1 in Africa, the phase 2 and 3 in the Indian Ocean, phases 4 and 5 in the Indonesian archipelago, phases 6 and 7 in the Pacific Ocean west and phase 8 in the western hemisphere (near America). Fig. 9 (A, B, C, D) show anomalous water vapor transport time of the MJO. It can be seen that in each drawing, the transport of water vapor moves toward convergence centers are characterized by wind direction. Convergence Center has the potential to become the center of cloud formation because of the removal of the air mass in the region. Water vapor in the air condenses to form water droplets gather into clouds. Center cloud formation can be indicated by a negative value of OLR anomalies in Fig. 9 (E, F, G, H). The Indonesian archipelago became the center of the divergence of water vapor transport while MJO phase 1 and 8. Fig. 9A shows the direction of the dominant moisture transport stir towards the northwest Indian Ocean and the Pacific Ocean to the east toward the center of convergence. This convergence center can be identified on the negative OLR anomalies in the northern part of the Indian Ocean and the Pacific Ocean Middle section (Fig. 9E). Water vapor when the MJO phases 2 and 3 (Fig. 9B), moving from the Pacific Ocean to the Indian Ocean. Water vapor Get together and form a cloud surrounding the Indian Ocean region to Sumatra and Java are marked with negative OLR (Fig. 9F). Instead, the MJO phases 4 and 5 (Fig. 9C) changed the direction of transport moisture from the Indian Ocean to Pacific Ocean. The amount of water vapor passing through Indonesia is quite large and evenly, so the clouds that form a very broad covering all the islands of Indonesia to the western Pacific Ocean (Fig. 9G). MJO phase regions 6 and 7 are in the western part of the Pacific Ocean. Fig. 9D shows the transport of water vapor is concentrated in the region and cloud thick (Fig. 9H) is in the same area. The water vapor transport convergence Pattern of all MJO events closely related to the formation of clouds in the path. But of all phases of the MJO, only phases 2,3,4, and 5, which could increase rainfall in the area of West Java, based on the direction of water vapor transport and OLR anomalies in this phase. 3.4. Effect of Cold Surge on Atmospheric Circulation in extreme rainfall events in Katulampa Cold surge is meridional winds at a speed greater than 8 m / s blowing towards the south at coordinates 15 LU and 1100 BT - BT 117.50 (Fig. 4). Hattori et al. [5] describe the incident there were 11 cold surges that significantly affect the increasing of rainfall in the Indonesian archipelago, especially in the north of the island of Java. Previous research suggests that the incidence of cold surge characteristics are very dependent on temperature and pressure conditions in the northern South China Sea. Other studies showed that there is no association between the occurrence of cold surge in the northern region of the South China Sea with the variation of convection in the equatorial region. Compo et al. [20] found a strong correlation between the cold surge in 150 LU, 1150 BT and convective activity in the southern part of Indonesia. Cold surge blows from mid-latitude regions, Indochina and Peninsular Malaysia to Indonesia through the South China Sea with a large mass of water vapor. This event will trigger the formation of convective clouds and can increase the intensity of rain in the Indonesian archipelago, especially Java Sea. Rainfall is not increased immediately when a strong surge of cold events, but an increasing in precipitation occurs when cold surge weakened. This happens because of the grace period shown in Fig. 10 which explains that the strong cold surge that occurred in the South China Sea today will enhance precipitation in Katulampa one day later. Takahashi [21] states that the low cloud formation is inversely related to the incidence of cold surge. The strong Cold surge will move water vapor in the region. But on the first day up to second after a cold surge crossed, began intensive cloud formation and the peak occurred on the fourth day when the cold surge has weakened.
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5 3 1 -1 -3 -5 -7 -9 -11 -13 -15
Rainfall
Meridional Wind
Meridional Wind (m/s)
180 160 140 120 100 80 60 40 20 0 12/25/1981 2/8/1983 2/7/1984 12/13/1986 2/11/1987 2/12/1989 12/19/1991 12/9/1992 12/2/1993 1/3/1996 12/2/1997 1/1/1999 1/26/2002 2/4/2003 1/23/2004 1/18/2005 2/16/2007 12/7/2007 12/3/2008 1/9/2010 12/24/2011 12/23/2012
Rainfall (mm)
Lag (-1)
Threshold
Fig. 10. Relationship of precipitation with meridional wind on cold surge events in extreme rainfall events in Katulampa.
4. Conclusion There are 86 DPE events on DJF 1981-2013 using Pencentile 95% with threshold value is 67 mm. MJO and Cold Surge trigger the strengthening of wind. The strengthening of wind affects the water vapor transport and triggers the convective cloud formation in the upper of Java Island. The influence of MJO to increase the rainfall in Katulampa occurs on phase 2,3,4,5. In term of Cold Surge, there is a time lag (1-2 days) between the occurrence of cold surge with the increasing of rainfall in Katulampa. The result shows that the regional atmospheric circulation (which is influenced by MJO and Cold Surge events) affects the increasing rainfall in Katulampa. Acknowledgements We would like to thank Dr. Akhmad Faqih and Dr. I Putu Santikayasa in providing the opportunities in enhancing our knowledge in Department of Geophysics and Meteorology. Great thanks to other people for their great support, translations and friendship. References 1. Irianto G. Implikasi Penyimpangan Iklim Terhadap Tataguna Lahan. Makalah Seminar Nasional Ilmu Tanah. KMIT Jurusan Tanah Fakultas Pertanian UGM. Yogyakarta (ID); 2003. In Bahasa. 2. Guan Z, Han J dan Li M. 2011. Circulation patterns of regional mean daily /precipitation extremes over the middle and lower reaches of the Yangtze River during the boreal summer. Climate Research, 2011;50(2-3):171–185. 3. Zhou TJ dan Yu RC. Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China. Journal of Geophysical Research D: Atmospheres, 2005;110(8):1–10. 4. Aldrian E dan Susanto RD. 2003. Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. Int. J. Climatol, 2003;23 (12):1435-1452. 5. Hattori M, Mori S, dan Matsumoto J. The cross-equatorial northerly surge over the maritime continent and its relationship to precipitation patterns. Journal of the Meteorological Society of Japan, 2011;89A:27-47. 6. Wu P, Hara M, Fudeyasu H, Yamanaka MD, Matsumoto J, Syamsudin F, Sulistyowati R, dan Djajadihardja YS. The impact of transequatorial monsoon flow on the formation of repeated torrential rains over Java Island. SOLA 2007;3: 93–96. 7. Komeji AD. Penentuan batas ambang curah hujan penyebab banjir (studi kasus DAS Ciliwung Hulu) [skripsi]. Bogor (ID): Institut Pertanian Bogor; 2012. In Bahasa. 8. [BAPPENAS] Badan Perencanaan Pembangunan Nasional. 2007. Laporan Prakiraan Kerusakan dan Kerugian Pasca Bencana Banjir di Wilayah Jabodetabek [internet]. [diacu 2015 Mei 31]. In Bahasa.
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9. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, dan Joseph D. 1996. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society. 10. Liebmann B dan Smith CA. Description of a complete (interpolated) outgoing longwave radiation dataset. Bulletin of the American Meteorological Society, 1996;77: 1275 –1277. 11. [IPCC] Intergovernmental Panel on Climate Change. 2007. IPCC Fourth Assessment Report: Climate Change 2007 [internet]. [diacu 2015 Mei 31]. Tersedia dari: https://www.ipcc.ch/publicationsand data/ar4/wg1/en/ch3s 3-8-5.html. 12. Walpole RE. 1995. Pengantar Statistika, Edisi ke-3. Jakarta (ID): PT. Gramedia Pustaka Utama. In Bahasa. 13. Holton JR. 2010. An Introduction to Dynamic Meteorology, 4th Edision. Elsevier Academic Press (USA). 14. Kantha L, Thaned R dan Josep L. The North Indian Ocean circulation and its variability as seen in a numerical hindcast of the years 1993– 2004. Progress in Oceanography, 2008;76:111–147. 15. Yamada H, Kunio Y, Masaki K dan Ryuichi S. Observations of a Super Cloud Cluster Accompanied by Synoptic-Scale EastwardPropagating Precipitating Systems over the Indian Ocean. Journal of the Atmospheric Sciences, 2010;67(5):1456–1473. 16. Zhou TJ dan Li ZX. Simulation of the east Asian summer monsoon by using a variable resolution atmospheric GCM. Clim. Dyn. 2002;19:167–180. 17. Zhou L dan Murtugudde R. Influences of Madden–Julian Oscillations on the eastern Indian Ocean and the maritime continent. Dynamics of Atmospheres and Oceans 2010;50:257–274. 18. Hidayat R dan Kizu S. Influence of the Madden–Julian Oscillation on Indonesian rainfall variability in austral summer. Int. J. Climatol. 2010;30: 1816–1825. 19. Wheeler M dan Hendon H. An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Mon. Wea. Rev., 2004;132:1917-1932. 20. Compo GP, Kiladis GN, dan Webster PJ. The horizontal and vertical structure of East Asian winter monsoon pressure surges. Quart. J. Roy. Meteor. Soc. 1999;125:29–54. 21. Takahasi HG. 2013. Orographic low-level clouds of Southeast Asia during the cold surges of the winter monsoon. Atmospheric Research 2013;131:22–33.
ScienceDirect Procedia Environmental Sciences 33 (2016) 155 – 166
The 2nd International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring 2015, LISAT-FSEM 2015
Extreme rainfall in Katulampa associated with the atmospheric circulation Gigih Bangun Wicaksono*, Rahmat Hidayat Departement of Geophysic and Meteorology, Faculty of Sains and Mathematic, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16680, West Java, Indonesia
Abstract Extreme rainfall will increase the river flow and it can be triggered by regional and global atmospheric circulation. Objective of this study is to analyze the intensity of rainfall in Katulampa and its associated with the atmospheric circulation. We found that the extreme rainfall threshold in Katulampa is 67 mm in December-January-February (DJF) which is determined using 95% percentile method. The number of extreme rainfall events is 86 days during 1981 to 2013. The results showed that increasing of rainfall intensity in Katulampa is influenced by the Madden-Julian Oscillation (MJO) and cold surge. MJO and cold surge modify the transport of water vapor and triggering the formation of convective clouds over the Java Island. Phase 2,3,4 and 5 of MJO have the highest impact on increasing rainfall intensity in Katulampa which is characterized by Outgoing Longwave Radiation (190 - 210 W/m2). Meanwhile, the cold surge event has time lag about 1-2 days to increase the rainfall intensity in Katulampa. © 2016 2016The TheAuthors. Authors. Published by Elsevier Published by Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of LISAT-FSEM2015. Peer-review under responsibility of the organizing committee of LISAT-FSEM2015 Keywords: atmospheric circulation; cold surge; extreme rainfall; madden-julian oscillation.
1. Introduction An increasing amount of attention has been given to atmospheric sciences such as the occurrences of weather and climate extremes. The occurrence of extreme events is influenced by climate variations on different time scales, which include intraseasonal oscillations, interannual variabilities and interdecadal changes [1, 2]. Weather and climate extreme events are often occurred in the tropical region including Indonesia. These conditions cause the complex system of atmospheric circulation in Indonesia maritime continent due to the influence of the monsoon. The
* Corresponding author. Tel.: +62-857-1096-0352. E-mail address: [email protected].
1878-0296 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of LISAT-FSEM2015 doi:10.1016/j.proenv.2016.03.066
156
Gigih Bangun Wicaksono and Rahmat Hidayat / Procedia Environmental Sciences 33 (2016) 155 – 166
occurrence of monsoons are influenced by differential heating between the ocean and land leading to differences in the amount of water vapor. Anomalous of the monsoonal precipitation are affected by water vapor transport. Zhou and Yu [3] stated that the large amounts of water vapor have an impact on rainfall anomalies that occur in the region through the monsoonal winds. Rainfall in Katulampa dam in the west Java is influenced by the Asia-Australia monsoon [4]. The Asian monsoon coincided with cold surge events increase rainfall in the north of Java Island significantly [5]. Wu et al. [6] pointed out that the strong northerly wind events occurred five times over the South China Sea in 2007. This strongest event enhances northerly wind blew across the equator, hence increasing rainfall over Java [6]. Research related to rainfall anomaly in Katulampa dam is needed to be assessed because Katulampa dam has an important role as the gates that keep the water flow from the Ciliwung Hulu watershed. Study of rainfall anomaly in Ciliwung Hulu watershed has done by Komeji [7]. He made a threshold extreme of Katulampa rainfall is 149 mm, which may cause flooding in Jakarta. Flood events in Jakarta cause huge economic losses. Bapennas [8] declared a total loss incurred when the Jakarta flood event in 2007 reached 5.16 trillion rupiah, with the potential economic loss reached 3.60 trillion rupiah. So, the objective of this study is to determine the extreme rainfall and analyzed the extreme rainfall events in Katulampa dam associated with the tropical atmospheric circulation. 2. Methods The data used are the Katulampa daily rainfall data (06º 38'S, 106º 50'E, with elevation of 347 meters above sea level) from BPSDA Ciliwung-Cisadene 1981-2013, Katulampa daily discharge data from the Central of CiliwungCisadane River Region 1992-2013, and five atmospheric parameters data as shown in Table 1 [9, 10]. Table 1. Atmospheric parameters data used for mapping. Resolution Data Type Temporal
Spatial (o)
Zoal wind
Daily
2,5 x 2,5
Meridional Wind
Daily
2,5 x 2,5
Surface pressure
Daily
2,5 x 2,5
Specific humidity
Daily
2,5 x 2,5
Outgoing Longwave Radiation (OLR)
2,5 x 2,5 Daily
Source
iridl.ldeo.columbia.edu/SOURCES /.NOAA/.NCEP-NCAR/.CDAS-1 (Kalnay et al. 1996) iridl.ldeo.columbia.edu/SOURCES /.NOAA/.NCEP/.CPC/.GLOBAL (Liebmann dan Smith 1996)
Following Guan et al. [2], we determine the threshold of extreme rainfall by using the 95% percentile method. The same method was done by the Intergovernmental Panel on Climate Change (IPCC) to determine the threshold of extreme precipitation [11]. The threshold is calculated using equation (1) as proposed by Walpole [12]. ݄ܶ ݈݄݀ݏ݁ݎൌ
ሺ݅ݔሺ݊ ͳሻ ͳͲͲ
(1)
Where i is percentile (1, 2, ..., 99) and n is number of data. Rainfall data must be ordered first from the smallest to the largest value and the value of precipitation <1 mm / day is not included in the calculation of the threshold. The threshold values were then obtained with the 95% percentile method. We also compared the threshold rainfall extreme that given from equation (1) with a threshold calculated by equation (2). ݄ܶ ݈݄݀ݏ݁ݎൌ ܽ ݈݈݂ܽ݊݅ܽݎ݂݁݃ܽݎ݁ݒ ሺʹ ݈݈݂ܽ݊݅ܽݎ݂݊݅ݐܽ݅ݒ݁݀݀ݎܽ݀݊ܽݐݏݔሻ
(2)
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Rainfall exceed the threshold are then categorized as extreme rainfall events. Extreme rainfall events during 1981-2013 will be recorded throughout date as the reference for collecting atmospheric parameters data to be used for composite map to explain the role of atmospheric conditions on the extreme rainfall events. Zonal and meridional wind data used to identify the direction and speed wind and the center of divergence / convergence which are calculated by equation (3) [13].
1 wu 1 wv v tanI a cosI wO a wI a where a is earth radius, I is latitude, λ is longitude, u is zonal wind, v is meridional wind. & & H vH
(3)
This study was confined to the Indonesian region, especially in the range of 200 S - 200 N and 600 E - 1800 E, the latitude angle tangent value is quite small compared to the radius of the earth (a), so that the third term of equation (3) can be ignored. Because a cosI dO = dx (zonal distance) and a dI =dy (meridional distance), then the equation (4) can be written as:
& & H vH
wu wv wx wy
(4)
Specific humidity data is used to estimate the water vapor transport which is calculated by equation (5) [3]. Therefore the outgoing longwave radiation (OLR) data is used to indicate the cloud condition. ଵ
௧
(5)
ܳ ൌ ௦ ܸ݀ݍ
where Q is moisture transport, g is gravitation, q is specific humidity, V is wind vector, dp is pressure changing, ps is surface pressure (1000 hPa), pT is top pressure (300 hPa). 3. Results 3.1. Threshold determination of extreme rainfall Extreme rainfall is rain events exceeding the threshold extreme calculated by equation (1) and (2). The threshold values resulting from the calculation method of 95% percentile and average method + (2 x standard deviation) have the results of each 67 mm and 66.7 mm, respectively (Table 2), so that these values are used for the determination of extreme rainfall. Daily rainfall distribution in 1981-2013 December-January-February (DJF) is depicted in Fig. 1. The rainfall exceeds the threshold extreme rainfall (black line) recorded as of the date of extreme rainfall events are shown in Table 3. Table 2. Threshold and the number of extreme rainfall events years 1981-2013 in Katulampa Method
Percentile 95%
Average + (2 x stdev)
Season
Threshold (mm)
Extreme numbers (days)
Threshold (mm)
Extreme numbers (days)
DJF
67
86
66.7
86
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Fig. 1. DJF rainfall and limits on extreme rainfall events in Katulampa years 1981 to 2013. Table 3. Date of extreme rainfall events on DJF in Katulampa the with 95% percentile method. Year 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
January 30 13 14, 17 12, 13 20 12 21, 30 26 3, 5 3 13, 24 1 25 29, 31 18, 23, 26 18 29 1, 17 20 9 15
February 8, 16 7 4 11, 27 25 12 8, 26 8 10 6 8, 26 3, 4, 7, 13 9 3, 16, 17 12, 17 22 -
December 25, 26 20 22 13, 27 3, 5 19, 24 9, 15 2 7 2 25 11, 19 3, 5, 7, 10 3 23 27 4 24, 27 8, 23 -
It is seen in Fig. 2 that inter-annual variability of frequency of extreme rainfall events in Katulampa. The number of extreme rainfall occurred about 2-3 days in a year. The largest number occurred in 2007 (8 days), while in 1990 did not occur the extreme rain in Katulampa.
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Fig. 2. The frequency of extreme rainfall events years 1981-2013 in Katulampa.
3.2. Atmospheric circulation in extreme rainfall events in Katulampa Rainfall will increase in case of heavy rains and evenly distributed. The amount of rainfall and its distribution is determined by the type of clouds, one of which is the convective clouds. Convective clouds are formed due to removal of air masses are large and focused on an area that is affected by atmospheric circulation (zonal and meridional wind movement).
A
B
Fig. 3. Surface pressure of the average DJF (a) and extreme rainfall events (b) years 1981-2013 in Katulampa.
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Naturally, the wind blows due to the pressure difference is from high pressure to the low pressure. Fig. 3a shows the distribution of regions with high and low pressure when the DJF (Climatology) and during extreme rainfall events observed in Katulampa. Fig. 3b shows the high (low) pressure is observed in the northern (southern) latitudes. These patterns are typically observing in DJF. However, the pressure over the South China Sea is increasing during extreme rainfall event. It implies that the occurrence of cold surge as variations between seasons of monsoon AsiaAustralia often observed in November to March [5]. Fig. 3a and 3b also show the pressure in Australia is lower than the southern Indian Ocean due to the heating in mainland. In addition, Fig. 3b shows the center of the cyclone observed in the Indian Ocean during the extreme rainfall events. Kantha et al. [14] and Yamada et al. [15] stated that frequency of cyclone centered in the Indian Ocean due to the transfer of energy towards the west along with the Asian monsoon, hence the movement of the wind will be focused in the regions.
A
B
Fig. 4. Wind on average DJF (a) and extreme rainfall events (b) years 1981-2013 in Katulampa.
Fig. 4a and 4b show wind direction during DJF season and the extreme rainfall events. The winds blow from the north to south through the South China Sea to the Strait Karimata and from south to north through the Indian Ocean region. The wind speed is increased during extreme rainfall event (Fig. 4b). Hattori et al. [5] argue that the increasing of wind speed is called a cold surge. Cold surge occurs due to the variations in pressure and wind speed over the South China Sea. Two-way movement of the wind brings moisture from the Pacific Ocean and from the South China Sea. Such moisture leads to convergence region and gradually enhancing the convective clouds and heavy rains in Jakarta [6].
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The wind is carrying huge moisture by transporting water vapor. This transport of water vapor is a large scale in the lower troposphere [16]. Fig. 5 shows the transport of water vapor during extreme rainfall events. Fig. 5a and 5b also show the highest water vapor transport in the Java Sea and the Indian Ocean. The Pacific Ocean and the South China Sea act as a source of water vapor. This centralized water vapor will trigger the formation of convective clouds in the cyclonic area.
Fig. 5. Transport of water vapor in extreme rainfall events
Center of convergence region and cloud formation can be identified from the Outgoing Longwave Radiation (OLR). The centers of convergence are shown in blue and red color in Fig. 6a and 6b. During the extreme events convergence center in the Indian Ocean is getting stronger.
A
B
Fig. 6. Divergence and convergence of surface winds on average DJF (a) and extreme rainfall events (b) years 1981-2013 in Katulampa.
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A
B
Fig. 7. Outgoing longwave radiation (OLR) on average DJF (a) and the extreme rainfall events (b) years 1981-2013 in Katulampa.
This convergence patterns show ITCZ happened in the southern hemisphere in DJF (Fig. 6). ITCZ is synonymous with the formation of convective clouds that may be suspected of OLR. OLR values indicate the thickness of the cloud. The smaller the value of OLR indicate clouds increasingly thick. This occurs because the grain of the water contained in the clouds can absorb the energy of long-wave radiation emitted by the earth surface. Fig. 7 shows the OLR in average conditions DJF and extreme events. Both of the images show the OLR around the Sunda Strait to the Java Sea less than 190 W / m2. This value indicates a thick cloud cover in the region that affects an increasing in the intensity of rainfall in Katulampa. However, in the event of extreme convective clouds are formed larger and centered on the Indian Ocean, Indonesian Islands to the Western Pacific Ocean as indicated by OLR values between 190-210 W / m2 (Fig. 7B).
Fig. 8. Outgoing longwave radiation anomalies on the extreme rainfall events in Katulampa.
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OLR anomalies (Fig. 8) obtained from the difference between the values of OLR in extreme rainfall events in Katulampa with an average value of OLR DJF months. OLR is negative indicates a clouds thicker. Visible cloud cover distributed throughout the Indonesian archipelago, but the West Java has a thickness greater than the cloud of other Indonesian areas indicated by anomalous OLR -5 W / m2. The Areas with the most density of cloud cover central Indian Ocean cyclonic happens when extreme rainfall events with OLR anomalies value of -15 W / m2. 3.3. Madden-Julian Oscillation influence on Atmospheric Circulation in extreme rainfall events in Katulampa Madden-Julian Oscillation (MJO) is the intra-seasonal energy transfer system on ocean-atmosphere interactions that have a significant impact on the atmospheric circulation in the Indian Ocean Island States of Indonesia, and the Pacific Ocean to the west. MJO occurrences may result in intra-seasonal variability and instability can strengthen the internal conditions that affect the response of the ocean circulation of the atmosphere above it. The atmospheric circulation will result in increased rainfall in the Indian Ocean region, the West Coast of Sumatra and Indonesia Island States [17].
Fig. 9. Transport of water vapor when the MJO [phase 1 and 8 (A), phases 2 and 3 (B), phase 4 and 5 (C), phases 6 and 7 (D)] and anomalous OLR when MJO [phase 1 and 8 (E), phases 2 and 3 (F), phase 4 and 5 (G), phases 6 and 7 (H)] in the extreme rainfall events in Katulampa.
Hidayat and Kizu [18] have examined the impact of the MJO variability of rainfall in Indonesia with the data of five daily average of rainfall during 1979-1990 from 31 stations throughout Indonesia rain, wind components (zonal,
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meridional, and vertical), and long-wave radiation. Rainfall in Indonesia increased by 1-3 mm / day on the mainland and 5 mm/day in the ocean during MJO events. This difference is due to the topography of the land factor is more varied than the sea, so that local factors more influential than the MJO. Anomalous conditions of water vapor transport and OLR when the MJO in extreme rainfall events in Katulampa are divided into several MJO phases based on the division of occurrence (Fig. 9). According to Wheeler and Hendon [19] MJO is divided into 8 regions, namely phase 1 in Africa, the phase 2 and 3 in the Indian Ocean, phases 4 and 5 in the Indonesian archipelago, phases 6 and 7 in the Pacific Ocean west and phase 8 in the western hemisphere (near America). Fig. 9 (A, B, C, D) show anomalous water vapor transport time of the MJO. It can be seen that in each drawing, the transport of water vapor moves toward convergence centers are characterized by wind direction. Convergence Center has the potential to become the center of cloud formation because of the removal of the air mass in the region. Water vapor in the air condenses to form water droplets gather into clouds. Center cloud formation can be indicated by a negative value of OLR anomalies in Fig. 9 (E, F, G, H). The Indonesian archipelago became the center of the divergence of water vapor transport while MJO phase 1 and 8. Fig. 9A shows the direction of the dominant moisture transport stir towards the northwest Indian Ocean and the Pacific Ocean to the east toward the center of convergence. This convergence center can be identified on the negative OLR anomalies in the northern part of the Indian Ocean and the Pacific Ocean Middle section (Fig. 9E). Water vapor when the MJO phases 2 and 3 (Fig. 9B), moving from the Pacific Ocean to the Indian Ocean. Water vapor Get together and form a cloud surrounding the Indian Ocean region to Sumatra and Java are marked with negative OLR (Fig. 9F). Instead, the MJO phases 4 and 5 (Fig. 9C) changed the direction of transport moisture from the Indian Ocean to Pacific Ocean. The amount of water vapor passing through Indonesia is quite large and evenly, so the clouds that form a very broad covering all the islands of Indonesia to the western Pacific Ocean (Fig. 9G). MJO phase regions 6 and 7 are in the western part of the Pacific Ocean. Fig. 9D shows the transport of water vapor is concentrated in the region and cloud thick (Fig. 9H) is in the same area. The water vapor transport convergence Pattern of all MJO events closely related to the formation of clouds in the path. But of all phases of the MJO, only phases 2,3,4, and 5, which could increase rainfall in the area of West Java, based on the direction of water vapor transport and OLR anomalies in this phase. 3.4. Effect of Cold Surge on Atmospheric Circulation in extreme rainfall events in Katulampa Cold surge is meridional winds at a speed greater than 8 m / s blowing towards the south at coordinates 15 LU and 1100 BT - BT 117.50 (Fig. 4). Hattori et al. [5] describe the incident there were 11 cold surges that significantly affect the increasing of rainfall in the Indonesian archipelago, especially in the north of the island of Java. Previous research suggests that the incidence of cold surge characteristics are very dependent on temperature and pressure conditions in the northern South China Sea. Other studies showed that there is no association between the occurrence of cold surge in the northern region of the South China Sea with the variation of convection in the equatorial region. Compo et al. [20] found a strong correlation between the cold surge in 150 LU, 1150 BT and convective activity in the southern part of Indonesia. Cold surge blows from mid-latitude regions, Indochina and Peninsular Malaysia to Indonesia through the South China Sea with a large mass of water vapor. This event will trigger the formation of convective clouds and can increase the intensity of rain in the Indonesian archipelago, especially Java Sea. Rainfall is not increased immediately when a strong surge of cold events, but an increasing in precipitation occurs when cold surge weakened. This happens because of the grace period shown in Fig. 10 which explains that the strong cold surge that occurred in the South China Sea today will enhance precipitation in Katulampa one day later. Takahashi [21] states that the low cloud formation is inversely related to the incidence of cold surge. The strong Cold surge will move water vapor in the region. But on the first day up to second after a cold surge crossed, began intensive cloud formation and the peak occurred on the fourth day when the cold surge has weakened.
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5 3 1 -1 -3 -5 -7 -9 -11 -13 -15
Rainfall
Meridional Wind
Meridional Wind (m/s)
180 160 140 120 100 80 60 40 20 0 12/25/1981 2/8/1983 2/7/1984 12/13/1986 2/11/1987 2/12/1989 12/19/1991 12/9/1992 12/2/1993 1/3/1996 12/2/1997 1/1/1999 1/26/2002 2/4/2003 1/23/2004 1/18/2005 2/16/2007 12/7/2007 12/3/2008 1/9/2010 12/24/2011 12/23/2012
Rainfall (mm)
Lag (-1)
Threshold
Fig. 10. Relationship of precipitation with meridional wind on cold surge events in extreme rainfall events in Katulampa.
4. Conclusion There are 86 DPE events on DJF 1981-2013 using Pencentile 95% with threshold value is 67 mm. MJO and Cold Surge trigger the strengthening of wind. The strengthening of wind affects the water vapor transport and triggers the convective cloud formation in the upper of Java Island. The influence of MJO to increase the rainfall in Katulampa occurs on phase 2,3,4,5. In term of Cold Surge, there is a time lag (1-2 days) between the occurrence of cold surge with the increasing of rainfall in Katulampa. The result shows that the regional atmospheric circulation (which is influenced by MJO and Cold Surge events) affects the increasing rainfall in Katulampa. Acknowledgements We would like to thank Dr. Akhmad Faqih and Dr. I Putu Santikayasa in providing the opportunities in enhancing our knowledge in Department of Geophysics and Meteorology. Great thanks to other people for their great support, translations and friendship. References 1. Irianto G. Implikasi Penyimpangan Iklim Terhadap Tataguna Lahan. Makalah Seminar Nasional Ilmu Tanah. KMIT Jurusan Tanah Fakultas Pertanian UGM. Yogyakarta (ID); 2003. In Bahasa. 2. Guan Z, Han J dan Li M. 2011. Circulation patterns of regional mean daily /precipitation extremes over the middle and lower reaches of the Yangtze River during the boreal summer. Climate Research, 2011;50(2-3):171–185. 3. Zhou TJ dan Yu RC. Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China. Journal of Geophysical Research D: Atmospheres, 2005;110(8):1–10. 4. Aldrian E dan Susanto RD. 2003. Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. Int. J. Climatol, 2003;23 (12):1435-1452. 5. Hattori M, Mori S, dan Matsumoto J. The cross-equatorial northerly surge over the maritime continent and its relationship to precipitation patterns. Journal of the Meteorological Society of Japan, 2011;89A:27-47. 6. Wu P, Hara M, Fudeyasu H, Yamanaka MD, Matsumoto J, Syamsudin F, Sulistyowati R, dan Djajadihardja YS. The impact of transequatorial monsoon flow on the formation of repeated torrential rains over Java Island. SOLA 2007;3: 93–96. 7. Komeji AD. Penentuan batas ambang curah hujan penyebab banjir (studi kasus DAS Ciliwung Hulu) [skripsi]. Bogor (ID): Institut Pertanian Bogor; 2012. In Bahasa. 8. [BAPPENAS] Badan Perencanaan Pembangunan Nasional. 2007. Laporan Prakiraan Kerusakan dan Kerugian Pasca Bencana Banjir di Wilayah Jabodetabek [internet]. [diacu 2015 Mei 31]. In Bahasa.
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