THE INFLUENCE OF THE MADDEN-JULIAN OSCILLATION
ON DIURNAL CYCLE OF RAINFALL OVER SUMATERA
RAHMI ARIANI
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY BOGOR
PERNYATAAN MENGENAI THESIS DAN SUMBER
INFORMASI SERTA PELIMPAHAN HAK CIPTA
Dengan ini, saya menyatakan bahwa thesis berjudul The Influence of the Madden-Julian Oscillation on Diurnal Cycle of Rainfall over Sumatera adalah benar karya saya dengan arahan dari komisi pembimbing dan belum diajukan dalam bentuk apa pun kepada perguruan tinggi mana pun. Sumber informasi yang berasal atau dikutip dari karya yang diterbitkan maupun tidak diterbitkan dari penulis lain telah disebutkan dalam teks dan dicantumkan dalam Daftar Pustaka di bagian akhir Thesis ini.
Dengan ini saya melimpahkan hak cipta dari karya tulis saya kepada Institut Pertanian Bogor.
Bogor, Juni 2014
Rahmi Ariani
RINGKASAN
RAHMI ARIANI. Pengaruh Osilasi Madden-Julian terhadap Siklus Harian Curah Hujan di Sumatera. Dibimbing oleh TANIA JUNE, AKHMAD FAQIH dan RAHMAT HIDAYAT.
Wilayah kepulauan Indonesia merupakan daerah yang paling intensif untuk proses konveksi dan curah hujan. Siklus diurnal yang diakibatkan oleh aktivitas konvektif sangat dominan di daerah daratan tropis dan menyumbang curah hujan paling besar di Indonesia. Keragaman aktivitas konveksi yang kuat di daerah tropis tidak hanya terjadi dalam skala waktu harian tapi juga dalam skala waktu intra-musiman. Gangguan skala besar di daerah tropis dalam hal aktivitas konvektif yang mempunyai peranan penting dalam mempengaruhi keragaman curah hujan dalam skala waktu intra-musiman dikenal dengan Osilasi Madden-Julian (Madden-Julian Oscillation, MJO). Pada studi ini, data TRMM V6 3B42 yang merupakan produk estimasi curah hujan dari data satelit digunakan untuk menganalisa pengaruh dari MJO terhadap siklus harian curah hujan di Sumatera. Selain itu juga dilakukan uji performa dari model RegCM4 dalam melakukan simulasi siklus harian dan kaitannya dengan MJO.
Karakteriktik siklus harian curah hujan didapatkan dengan menghitung klimatologi dari curah hujan pada musim hujan. Sedangkan untuk mengetahui pengaruh MJO terhadap siklus harian, dilakukan band pass filter terhadap data curah hujan dengan menggunakan Lanczos filter weights dengan rentang frekuensi 20 sampai 90 hari. Setelah itu, data tersebut dikomposit untuk mendapatkan anomali curah hujan pada masing-masing fase MJO. Kejadian MJO dari tahun 2000-2010 diindentifikasi dengan menggunakan kriteria, yaitu: nilai dari indeks MJO lebih besar dari pada satu dan kejadian MJO harus terjadi berturut-turut dari fase 1-8. Pada studi ini, analisis dibatasi pada musim hujan di Indonesia (Oktober-Maret) karena MJO menunjukkan signal yang paling kuat pada periode ini. Kejadian MJO dari tahun 2000-2010 yang bisa diidentifikasi adalah 18 kejadian. Data ini kemudian dikomposit untuk mendapatkan anomali pada masing-masing fase MJO.
Curah hujan di daratan mencapai puncaknya pada sore hari, sedangkan di lautan mencapai puncaknya pada malam hari. Osilasi Madden-Julian mempengaruhi siklus harian curah hujan di Sumatera dengan memperkuat (melemahkan) siklus harian curah hujan pada saat fase aktif (non-aktif) dan mengubah waktu puncak hujan di lautan. Pada saat fase aktif (non-aktif), MJO meningkatkan (menurunkan) curah hujan di darat dan di laut berturut-turut sebesar 33-46% (21-44%) dan 26-64% (32-54%) terhadap rata-rata klimatologi. Puncak curah hujan di daerah lautan terjadi dua kali pada fase 2 (18 LT dan 00 LT) dan fase 3 (21 LT dan 3 LT), sedangkan pada fase non-aktif curah hujan di lautan hampir tidak pernah terjadi atau terukur sangat kecil.
MJO. Hal ini mengindikasikan bahwa model lebih baik dalam melakukan simulasi variasi intra-musiman dibandingkan dengan variasi harian. Penelitian lebih lanjut dengan konfigurasi model yang berbeda dibutuhkan karena masing-masing skema akan mempunyai performa yang berbeda pada daerah yang berbeda.
SUMMARY
RAHMI ARIANI. The Influence of the Madden-Julian Oscillation on Diurnal Cycle of Rainfall over Sumatera. Under supervision of TANIA JUNE, AKHMAD FAQIH and RAHMAT HIDAYAT.
The Indonesian maritime continent is the most active region for convection process and rainfall. The references suggested that the rainfall in maritime continent is mostly caused by the convective activity associated with a diurnal cycle. The variability of deep convection over tropical regions operates not only on a diurnal cycle time scale but also at intra-seasonal time scale. At intra-seasonal time scale, the large scale disturbance in the tropical region in terms of convective activity, which plays important role on rainfall variability, known as Madden-Julian Oscillation(MJO). In this study, TRMM V6 3B42 satellite rainfall estimation product was employed to analyze the influence of the Madden-Julian Oscillation (MJO) on the diurnal cycle of rainfall over Sumatera. Moreover, we also tested the performance of RegCM4 in simulating diurnal cycle and the association with the MJO.
The climatology of the seasonal rainfall (rainy season) was derived to study the characteristics of diurnal rainfall variation over Sumatera. To study the impact of MJO on diurnal cycle of rainfall, the rainfall was band pass-filtered using Lanczos filter weights with a cut-off frequency range from 20 to 90 days. The composite analysis was used to obtain the anomalies of rainfall in each MJO phase. The MJO events during 2000-2010 periods are identified using two criteria: first, the amplitude of the MJO index must be greater than one and second, the event of MJO need to be sequentially occurred from phase 1 to 8. The analysis was confined to the rainy season in Indonesia (October-March) because the MJO shows its strongest signal during this period. The MJO events identified during the period of 2000-2010 are 18 events. The composite dataset was made for each MJO phase.
The rainfall over land reaches a maximum in the afternoon, while over sea maximum occurs in the night time. The MJO modulates the diurnal cycle of rainfall around Sumatera by enhancing (reducing) the diurnal cycle of rainfall during its active (inactive) phase and altering the time of rainfall peak over the ocean. During its active (inactive) phase, the MJO increases (decreases) the amplitude of rainfall over land and over ocean by 33-46% (21-44%) and 26-64% (32-54%) to climatological mean, respectively. The rainfall peak over the ocean occurs twice during phase 2 (18 LT and 00 LT) and phase 3 (21 LT and 3 LT), while during inactive phase rainfall over the ocean never occurred.
The RegCM4 simulation demonstrates that the model was able to capture the land-sea contrast of diurnal cycle but unable to capture the time of rainfall peak. The rainfall peak comes earlier compared to TRMM. On the other hand, the model is able to capture the increase (decrease) of rainfall during the active (inactive) phase of MJO. This indicates that model is better in simulating the intra-seasonal cycle variation than the diurnal cycle. Further research of different model configuration is needed as the different schemes have different performance over different regions.
© Hak Cipta Milik IPB, Tahun 2011
Hak Cipta Dilindungi Undang-Undang
Dilarang mengutip sebagian atau seluruh karya tulis ini tanpa mencantumkan atau menyebutkan sumbernya. Pengutipan hanya untuk kepentingan pendidikan, penelitian, penulisan karya ilmiah, penyusunan laporan, penulisan kritik, atau tinjauan suatu masalah; dan pengutipan tersebut tidak merugikan kepentingan IPB
THE INFLUENCE OF THE MADDEN-JULIAN
OSCILLATION ON DIURNAL CYCLE OF RAINFALL OVER
SUMATERA
RAHMI ARIANI
Thesis
as one of the requirements to obtain a degree of Magister Science
at
Major of Applied Climatology
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
Thesis Title : The Influence of the Madden-Julian Oscillation on Diurnal Cycle of Rainfall over Sumatera
Name : Rahmi Ariani Student ID : G251110051
Approved by Supervisor Commission
Dr Ir Tania June, MSc Head of Supervisor
Dr Akhmad Faqih, SSi Dr Rahmat Hidayat
Co-Supervisor Co-Supervisor
Head of Major Dean of Graduate School
Applied Climatology
Dr Impron, MScAgr, SSi Dr Ir Dahrul Syah, MScAgr
PREFACE
The author would like to say the highest gratitude to Allah S.W.T for giving the opportunity to accomplish the thesis entitle “The Influence of the Madden-Julian Oscillation on Diurnal Cycle of Rainfall over Sumatera”. The MJO impacts the rainfall variability in Indonesia, causing flood and drought that lead to the hardship for million people, especially for those who depends their income on agricultural sector. It has widely known that extreme rainfall and dry event can cause crop failures. Therefore, the analysis of the influence of MJO on diurnal cycle of rainfall could be used to help to prevent the devastating flood or drought events that caused by MJO since it can improve the climate and weather forecasting.
In this opportunity, many thanks are given to those who helped this study can possibly conducted and accomplished. The Directorate General of Higher Education, Ministry of National Education, Indonesia (DIKTI) that sponsors author’s master degree under Beasiswa unggulan scholarship. The Supervisors committee: Dr. Tania June, Dr. Akhmad Faqih and Dr. Rahmat Hidayat for the constructive comments and suggestions to construct this study. Helpful discussions and support from Sandro Wellyanto Lubis. Andrea Bache for editing the manuscript. The Center for Climate Risk and Opportunity Management in South East Asia and Pacific (CCROM-SEAP) for the kind permission to use RegCM4 model and the help of their staff in installing linux. The Collaborative Research Centre 990 (CRC990) project group A3 that support this research topic. Friends from KLI 2011 and Summer Course 2012 for the moral supports and various helps during this study.
This thesis is dedicated to author’s parents and family which become the inspirations and motivation to author. Without their support in many ways, it is impossible for author to accomplish this study and get through the difficult time in life. Thus, author would like to say the high gratitude to them. The author realizes that this thesis still far from perfect, therefore the comments and input will be appreciated. Hopefully, this thesis can be useful and benefits for the readers.
Bogor, June 2014
TABLE OF CONTENTS
LIST OF FIGURES vii
LIST OF APPENDICES viii
1 INTRODUCTION 1
Background 1
Statement of Problem 2
Objective 2
Benefit of Study 2
2 LITERATURE REVIEW 3
The Scale Interaction between Diurnal Cycle and Madden-Julian Oscillation 3
Regional Climate Model RegCM4 4
3 DATA AND METHODS 5
Research Location 5
Data 5
Analysis Procedure 5
4 RESULT AND DISCUSSION 13
The Characteristic of Diurnal Cycle over Sumatera 13
Modulation of Diurnal Cycle of Rainfall by the MJO 14
The Role of Topography Weakens the MJO Signal 18
RegCM4 Performance in Simulating Diurnal Cycle of Rainfall and MJO 19
5 CONCLUSION AND SUGGESTION 25
Conclusion 25
Suggestion 25
REFERENCES 26
APPENDICES 29
LIST OF FIGURES
1 The Research domain with topography shaded (a) TRMM (b) RegCM4 simulation. The black dots denote the spots for analysis in figure 8. The dashed box indicated the area used for time-longitude plot. 6
2 The Taylor Diagram (Taylor 2001). 11
3 Spatial distribution of TRMM rain rate (mm/hour) for period 2000-2010
(a)03LT (b)09LT (c)15 LT (d)21LT. 13
4 Time-longitude plot of diurnal variation (mm/hour) for period 2000-2010. Averaged over region 2.5⁰S-2.5⁰N. The red dashed lines indicate the coastline
of Sumatera. 14
5 Composite of rain rate anomalies (mm/hour) in each MJO phase at (a) 09 LT (b)15LT (c)21LT (d) 03 LT during boreal winter. 16
6 Time-longitude plot of composite diurnal variation of rainfall in each MJO phase (a-h). Averaged over area 2.5⁰S-2.5⁰N. The red dashed lines indicate
the coastline of Sumatera. Unit is mm/hour. 17
7 Composite of rainfall anomalies in each MJO phase (a) over land (b) over ocean. The fraction of decrease and increase of rainfall (c) over land (d) over
ocean to climatological mean. 18
8 Composite rainfall anomalies at 15 LT in each MJO phase Averaged over
area denoted in figure 1. 19
9 Spatial distribution of rain rate (mm/hour) simulated by RegCM4 for period
2000-2010. 20
10 Time-longitude plot of diurnal variation (mm/hour) simulated by RegCM4. Averaged over region 2.5⁰S-2.5⁰N. The red dashed lines indicate the coastline
LIST OF APPENDICES
1 TRMM V6 3B42 Precipitation Product Algorithm 29 2 The RegCM4 configuration (the model input namelist file) 30
3 Roadmap of MJO 32
4 Research Flowchart 33
5 The sample of filtered data anomaly compared to daily mean anomaly 34 6 Location of four samples area in Figure 11 35 7 The Correlation, RMSE and ratio of Variance showed in Taylor Diagrams
1 INTRODUCTION
Background
The Indonesian maritime continent, which lies in the equatorial region, receives abundant solar radiation that leads to the development of deep convection. The complex topography of the islands and warm seawater surrounding it are favorable for the diurnal cycle of deep convection system due to the difference of thermal properties between land and sea surface. Hendon and Woodberry (1993) suggested that the diurnal amplitude of deep convection is significant over tropical landmasses, and other studies showed that rainfall in the maritime continent is mostly caused by the convective activity associated with a diurnal cycle (Yang and Slingo 2000, Nezbitt and Zipser 2003). The diurnal cycle of convection is not only prominent local phenomenon, but also play important role in maintaining planetary-scale atmospheric circulation. It is the heat engine which provides the energy through latent heat release of condensation.
The variability of deep convection over tropical regions operates not only on a diurnal cycle time scale but also at intra-seasonal time scale. The Madden-Julian Oscillation (MJO) is a large scale disturbance in the tropical region in terms of convective activity. The MJO that propagates eastward is associated with the large-scale deep convective activity and affects rainfall variability over the tropical region and higher latitudes (Chen and Houze 1997, Hidayat and Kizu 2009, Tian et al. 2006, Donald et al. 2006). While earlier studies have investigated the interaction between the MJO and diurnal cycle (Sui and Lau 1992, Chen and Houze 1997, Tian et al. 2006, Fujita 2011), the impact of the diurnal cycle in particular region with different characteristics such as Sumatera still needs to be investigated. Nezbitt and Zipser (2003) pointed out that the diurnal cycle varies significantly among land region depending on topography of the region.
In this study, the analysis was conducted to investigate the characteristic of the diurnal cycle over Sumatera and how the MJO modulates the diurnal cycle of rainfall. Moreover, the regional climate model RegCM4 was utilized as an example of the result’s application to model evaluation. Diro et al. (2012) investigated the model performance by simulating the RegCM4 over Central America and found that the model was unable to capture the peak time of diurnal cycle of precipitation. Giorgi (2011) suggested that although the RegCM4 shows an improvement compared to the previous version, the further testing of different model configuration is needed since the different schemes have different performance over different region.
Statement of Problem
1. How are the characteristics of the diurnal cycle of rainfall over Sumatera? 2. How does the MJO modulate the diurnal cycle of rainfall over Sumatera
during rainy season (October-March)?
3. Does regional climate model RegCM4 well simulate the diurnal cycle of rainfall over Sumatera and the intra-seasonal cycle such as MJO using the selected model configuration? How good the model performance compared to observation?
Objective
1. Study the characteristics of diurnal cycle of rainfall over Sumatera. 2. Investigate the MJO influence on diurnal cycle of rainfall.
3. Simulate diurnal cycle of rainfall and its association with MJO using regional climate model RegCM4
Benefit of Study
2 LITERATURE REVIEW
The Scale Interaction between Diurnal Cycle and Madden-Julian Oscillation
Madden-Julian Oscillation is a planetary scale phenomenon with intra-seasonal time scale whereas diurnal cycle is a local or regional scale with daily time scale. Yet, both of them are two fundamental features of deep convection variability in the tropics. Several studies have investigated the scale interaction between them (Sui and Lau 1992, Chen and Houze 1997, Tian et al. 2006, Fujita 2011). Although previous studies have been conducted to understand the impact of the MJO on the diurnal cycle their results do not agree. Moreover, the domain of research is different for each study.
Sui and Lau (1992) investigated relationship between the diurnal cycle and intra-seasonal over the Maritime Continent and found that during the active periods of the MJO, the diurnal cycle was diminished by MJO and vice versa. Contrary to Sui and Lau (1992), Chen and Houze (1997) that investigated the diurnal cycle of tropical deep convection over the western Pacific warm pool region during the Tropical Ocean Global Atmosphere Coupled Ocean– Atmosphere Response Experiment (TOGA COARE) reported that cloud systems are spatially larger and their lifetime is longer during the active phase of MJO. They reach maximum in the night time until dawn and decay after sunrise. Meanwhile, cloud systems are small and their lifetimes relatively short during the inactive phase of MJO. The cloud systems reach maximum in the afternoon and then dissipate. Tian et al. (2006) demonstrated that the diurnal cycle of tropical deep convective cloud is enhanced (reduced) over both land and ocean during the convectively enhanced (suppressed) phase of MJO. Fujita (2011) that investigated the diurnal convection peak over the eastern Indian Ocean off Sumatera during different phase of MJO suggested that while the atmosphere over the eastern Indian Ocean contains abundant water vapor fairly well heated by solar radiation created the favorable condition for the development of two diurnal convection peaks, i.e. the evening convection over the land induced by solar radiative heating and the midnight convection over the ocean triggered by convergence of the low-level westerly wind and the land breeze.
Sumatera is one of the largest island in Indonesia with complex topography where the mountain range with average height of 2000 meter lies along its southwestern coastline (Figure 1). The previous studies found the diurnal migration of cloud over Sumatera which indicated that the rainfall system in the island follows a clear diurnal cycle (Mori et al. 2004, Sakurai et al. 2005, Hamada
Regional Climate Model RegCM4
The RegCM system is a community model coordinated by the Earth System Physics section of the Abdus Salam International Centre for Theoretical Physics (ICTP; Giorgi et al. 2006). It has been used for a wide range of studies, from process studies to paleo-climate and future climate simulation (Qian 2010, Gu et al. 2012, Wook and Gyu 2012, Notaro et al. 2013). It is also a tool for downscaling, which is a method for obtaining high-resolution climate or climate change information from coarse-resolution global climate models. RegCM4 was released by the ICTP in April 2011 as a first complete version (RegCM4.1).
Regional climate model is receiving increased attention in diurnal cycle of rainfall because it is not well demonstrated yet. Whereas, a good simulation of the diurnal cycle is important to represent the mean climate and the variability on a longer time scales (Yang & Slingo 2001). A numerous studies have studied the diurnal cycle using the regional climate models (Yang and Slingo 2001, Dai and Trenberth 2004, Diro et al 2012, Fuente-Franco et al 2013). The errors in the diurnal cycle which related to the convection trigger affect the intensity and the frequency of simulated precipitation. Many of convection schemes allow the convection to start very early in the day (Dai and Trenberth 2004), resulting in overestimates of light rainfall events. Dai and Trenberth (2004) suggested that such errors can be masked in monthly mean or longer time scale rainfall statistics though it may contributes to biases in the other fields, such as cloudiness. The errors in simulating the diurnal cycle is caused by the model deficiencies in physical parameterizations such as boundary layer and convective parameterization which related to the surface heating (Diro et al. 2012). Fuente-Franco et al (2013) which analyzed the diurnal cycle over Mexico using RegCM4 found that the precipitation was overestimated over the mountainous region due to the high frequency of low-precipitation events. They also suggested that the biases may caused by the anomalies in capturing the shift of ITCZ position along with the shortcoming of model parameterization of convection over the mountainous region.
3 DATA AND METHODS
Research Location
The research was carried out in the laboratory of Climatology, Department of Geophysics and Meteorology, Bogor Agricultural University and Center for Climate Risk and Opportunity Management in Southeast Asia and Pacific (CCROM-SEAP) from February 2013 to March 2014. The domain of research areas was between 8º S - 8º N and 92º E -109º E (Figure 1) from period of 2000-2010.
Data
The data that we employed to examine the diurnal cycle characteristic and the influence of the MJO in this study is TRMM V6 3B42 precipitation data product. This TRMM has 3 hourly temporal resolution and 0.25ºx0.25º spatial resolution. It integrates three sensors (VIRS, TMI, and PR) with merged Infra-Red rain rate data and the Global Precipitation Index (GPI) (Huffman et al. 2007; http://www.mirador.gsfc.nasa.gov). High temporal and spatial resolution of the data and the algorithm used to obtain the product makes the product suitable to study the diurnal characteristic of rainfall and its association with MJO. Huffman
et al. (2007) showed that a diurnal cycle of TRMM product has a slight difference in phase and amplitude with gauge observations (the algorithm of TRMM 3B42 V6 Product can be seen in Appendix 1). To run the RegCM4 simulation, two observational data were used: (1) The Optimum Interpolation Weekly Sea Surface Temperature (SST oi_wk) from NOAA’s website (http://www.esrl.noaa.gov) with spatial resolution 1° x 1° (Reynolds et al. 2002) and NCEP/NCAR Re-analysis Project Version 1 (NNRP 1) with spatial resolution 2.5° x 2.5° and time resolution 8 hours (Kalnay et al. 1996; http://www.esrl. noaa.gov). In order to identified the MJO events, we used the index so-called Real Time Multivariate MJO (RMM1 and RMM2; http://cawcr.gov.au/staff/mwheeler/maproom/RMM/; wheeler and Hendon 2004. The periods of all the data were from 2000 to 2010.
Analysis Procedure
1. Diurnal Cycle Characteristic Analysis
(a) (b)
Figure 1 The Research domain with topography shaded (a) TRMM (b) RegCM4 simulation. The black dots denote the spots for analysis in figure 8. The dashed box indicated the area used for time-longitude plot.
2. Band-Pass Filter and Composite Analysis
To examine the impact of MJO on diurnal cycle of rainfall, the data was band pass-filtered using Lanczos filter. The filter is the fourier method of filtering the digital data by the given number of weights and the value of cut-off frequency (Duchon 1979). The purpose of this filter is to produce the new data sequence in the frequency domain which have been modified by applying the set of weight into the given data. In the other words, the weight will affect the amplitude of the data in the frequency domain. The digital filtering involves transforming the input data sequence xi, where t is time, into an output data sequence yi using the linier relationship:
∑
In which the wkare the suitable chosen weight. The effect of filtering data is best observed in the frequency domain. The minimum number of weight required to achieve best response in frequency domain is determined by the equation below (Duchon 1979):
Where the fc2 and fc1 is the frequency domain. In this study, the frequency domain
the filtered data. The composites are made from 18 MJO events identified during the period of 2000-2010 to obtain the anomalies of rainfall in each MJO phase.
3. RegCM4 Simulation and Configuration
The regional climate model RegCM4 was utilized to simulate the diurnal cycle of rainfall and its association with the MJO. It is the latest version of the International Centre for Theoretical Physics (ICTP) regional climate model. In order to provide the initial conditions and lateral boundary condition of the model, the Optimum Interpolation Weekly Sea Surface Temperature (SST oi_wk) from NOAA with spatial resolution 1° x 1° (Reynolds et al. 2002) and NCEP/NCAR
Re-analysis Project Version 1 (NNRP 1) with spatial resolution 2.5° x 2.5° and time resolution 8 hours (Kalnay et al. 1996) was employed. RegCM4 is a hydrostatic model with terrain following sigma coordinates and the horizontal grid Arakawa-Lamb B. The model calculates the radiative transfer with the radiative scheme of the global model CCM3 (Kiehl et al. 1996) and depicts the Planetary Boundary Layer (PBL) processes using modified version of the PBL scheme of Holtslag et al. (1990). The model employs the large-scale precipitation scheme of Pal et al. (2000) and uses the Community Land Model (CLM; Steiner et al. 2009) to describe the land surface processes. The convective precipitation scheme that we used in this study was the MIT convection parameterization of Emanuel (1991; see the details of model configuration in Appendix 2). In order to provide the analysis of diurnal cycle and its association with MJO, the RegCM4 was processed in a similar manner to TRMM data.
The details of RegCM4 physical parameterization can be described as follows:
a) Radiation Scheme
Radiative transfer calculations are carried out with the radiative transfer scheme of the global model CCM3 (Kiehl et al. 1996). The solar component follows the d-Eddington approximation of Kiehl et al. (1996). It takes into accounts the calculations for the short-wave and infrared parts of the spectrum, which includes 18 spectral intervals from 0.2 to 5 μm, including both atmospheric gases and aerosols. The scheme also includes contributions from all main greenhouse gases such as H2O, CO2, O3, CH4, N2O, and CFCs. The cloud scattering and absorption of solar radiation by aerosols are also included based on the aerosol optical properties. The parameterization follow that of Slingo (1989), whereby the optical properties of the cloud droplets (extinction optical depth, single scattering albedo, and asymmetry parameter) are expressed in terms of the cloud liquid water content and an effective droplet radius. “When cumulus clouds are formed, the gridpoint fractional cloud cover is such that the total cover for the column extending from the model-computed cloud-base level to the cloud-top level is a function of horizontal gridpoint spacing. The thickness of the cloud layer is assumed to be equal to that of the model layer, and a different cloud water content is specified for middle and low clouds” (Elguindi et al. 2010).
b) Land Surface Model
energy, momentum, water, and carbon. This scheme is developed by the National Center of Atmospheric Research (NCAR) as part of the Community Climate System Model (CCSM). The CLM divides the cell area into three sub-grib, i.e. the first sub-grid hierarchy composed of land units such as glacier, wetland, lake, urban, and vegetated land cover, and the second and third sub-grid hierarchy for vegetated land units, including different snow/soil columns for the different vegetation fractions, and plant functional types (PFT). The vegetated fractions are further divided into 17 different plant functional types. Biogeophysical processes are calculated for each land unit, column, and PFT separately and then averaged. “CLM3 biogeophysical calculations include a coupled photosynthesis–stomata conductance model, in-canopy radiation schemes, revised multi-layer snow parameterizations, and surface hydrology including a distributed river runoff scheme” (Oleson et al. 2008). Hydrological and energy balance equations are calculated for each land cover type and restore to the grid cell level. Soil temperature and water content are solved with the use of a multiple layer model.
c) Planetary Boundary Layer Scheme
The planetary boundary layer (PBL) scheme in RegCM4 is developed by Holtslag et al. (1990). In the Holtslag scheme, a PBL height is first calculated based on the iteration procedure of bulk critical Richardson number formulation. The non-local vertical profile of eddy diffusivity for heat, moisture, and momentum is specified from the surface to the PBL height, and a counter-gradient fluxes which resulting from large-scale eddies in an unstable atmosphere, is added for temperature and moisture. The eddy diffusivity depends on the friction velocity, height, Monin-Obhukov length, and PBL height. The vertical eddy flux within the PBL is given by:
(3)
where
γ
c is a “countergradient” transport term describing nonlocal transportdue to dry deep convection. The eddy diffusivity is given by the nonlocal formulation:
(4)
where k is the von Karman constant; wt is a turbulent convective velocity that
depends on the friction velocity, height, and the Monin–Obhukov length and h is the PBL height. The countergradient term for temperature and water vapor is given by:
(5)
where C is a constant equal to 8.5, and Φc0 is the surface temperature or water
For the calculation of the eddy diffusivity and counter-gradient terms, the PBL height is diagnostically computed from equation:
(6) Massachusetts Institute of Technology (MIT) scheme (Emanuel 1991). In this parameterization, cloud mixing is assumes to be episodic and inhomogenous (as opposed to a continuousentraining plume), and convective fluxes are based on a model of sub-cloud-scale updrafts and downdrafts. Convection is triggered when the level of buoyancy is higher than the cloud base level. Between these two levels, air is lifted and a fraction of the condensed moisture forms rainfall while the remaining fraction forms the cloud. “The cloud is considered to mix with the air from the environment according to a uniform spectrum of mixtures to their respective levels of neutral buoyancy. The mixing entrainment and detrainment rates are functions of the vertical gradients of buoyancy in clouds. The fraction of the total cloud base mass flux that mixes with its environment at each level is proportional to the undiluted buoyancy rate of change with altitude. The cloud base upward mass flux is relaxed towards the sub-cloud layer quasi equilibrium. Rainfall is based on auto-conversion of cloud water into rain water and accounts for simplified ice processes” (Elguindi et al. 2010).
e) Large-Scale Precipitation Scheme
The scheme is based on the Subgrid Explicit Moisture Scheme (SUBEX) which used to handle non-convective clouds and rainfall resolved by the model. The SUBEX parameterization of Pal et al. (2000) includes a prognostic equation for cloud water. It first calculates fractional cloud cover at a given grid point based on the local relative humidity. The fraction of the grid cell covered by clouds (FC) is calculated by,
(7) where RHmin is the relative humidity threshold at which clouds begin to form,
and RHmax is the relative humidity where FC reaches unity. FC is assumed to
be zero when RH is less than RHmin and unity when RH is greater than RHmax.
(8) where 1/Cppt considered the characteristic time for which cloud droplets are
converted to raindrops. The threshold is obtained by scaling the median cloud liquid water content equation according to the following:
(9)
where T is temperature in degrees Celsius, and Cacs is the auto-conversion
scale factor. Precipitation is assumed to fall instantaneously.
SUBEX also includes simple formulations for raindrop accretion and evaporation. The formulation for the accretion of cloud droplets by falling rain droplets is based on the work of Beheng (1994) and is as follows:
(10)
where Pacc is the amount of accreted cloud water, Cacc is the accretion rate
coefficient, and Psum is the accumulated precipitation from above falling
through the cloud. Precipitation evaporation is based on the work of Sundqvist et al. (1989) and is as follows: results of RegCM4 estimation with TRMM satellite-observation. We use the Taylor diagram to measure how well the model estimates the diurnal cycle of rainfall. Taylor diagrams provide a visual framework of statistical summary of correlation, root-mean-square difference and the ratio of the variance between the model and observations (Taylor 2001). It is comparing model results to observations and show how close the pattern of the model resembles observations. Taylor diagram has been used as the model performance evaluation (IPCC 2001).
The formulas for calculating the correlation coefficient (R), the centered RMS difference (E'), and the standard deviations of the model (σf) and the observation (σr) are given below (Taylor 2001) :
(12)
(13)
(15)
The Taylor diagram can represent three different statistics (the centered RMS difference, the correlation, and the standard deviation) in the two-dimensional space because these statistics are related by the following formula (Taylor 2001):
(16)
where R is the correlation coefficient between the model and observation, E' is the centered RMS difference, and σf 2and σr 2are the variances of the model and
observation, respectively. The Figure 2 displaying the Taylor diagram which shows the correlation coefficients, the standard deviation and RMS error (RMSE). The RMSE is indicated by the dashed line and measured by the distance from the reference point. The simulated patterns are considered agreed well with observation if the values lie between the point “reference” on the x-axis. At this point, the model has relatively high correlation and low RMSE. The value which lies on the dashed arc from reference point has the correct standard deviation (similar standard deviation as the observation).
5. MJO Events Identification
4 RESULT AND DISCUSSION
The Characteristic of Diurnal Cycle over Sumatera
Figure 3 shows the diurnal variation of rainfall over Sumatera Island at six hour interval from 11 years of climatology (rainy season). The figure indicates that there is a clear contrast of rainfall peak between the land and the ocean around Sumatera. The terrestrial rainfall reaches its maximum intensity in the afternoon (15 LT) and minimum in the early morning (3 LT). Meanwhile, rainfall over the ocean reaches its maximum during the night time (21 LT) and minimum in the afternoon (15 LT). The rainfall in the afternoon was prominent around the mountain range of Sumatera, indicating the terrestrial rainfall was formed by the orographic force and sea breeze induced by radiative heating that forms the convective cloud during the day time. This is consistent with the previous studies about the diurnal variation in the tropical region that the rainfall peaks in the afternoon over land (Nitta and Sekine 1994, Chen and Takahashi 1995, Ohsawa et al. 2001). Meanwhile, the maximum rainfall over the ocean occurred earlier compared to previous studies, where it occurred in the late midnight to the early morning. The difference of time peak of diurnal variation could be the effect of topography of Sumatera since diurnal convection variations and rainfall strongly depend on local topographic conditions.
The amplitude of terrestrial rainfall is larger than that of the ocean. The previous works comparing the tropical rainfall agreed that the amplitude of the diurnal cycle of terrestrial rainfall is larger than over oceans (Gray and Jacobson 1977, Yang and Slingo 2001). It is caused by the radiative heating during the day that brings abundant water vapor from Indian Ocean by the sea breeze to the land of Sumatera. Mori et al. (2004) investigated the fraction of the rainfall type in the Sumatera. The study concluded that 70% of rainfall over the land is caused by convective clouds, while the rainfall in the night time and early morning over ocean is equally caused by the convective and statiform cloud. Thus the rainfall in the night time is not as heavy as in the day time.
Figure 4 shows the diurnal variation of rainfall at time coverage 3 hour (climatology of rainy season). The rainfall with intensity above 0.4 mm/hour is first observed over land in the morning (09 LT) and reaches its peak in the afternoon (15 LT) around the mountainous region of Sumatera. In the early evening, the rainfall begins to migrate toward the western coastline and offshore region of the Indian Ocean. Meanwhile, the eastward migration begins in the late night (after 21 LT). The westward migration reaches rainfall peak in the offshore region at 21 LT while the eastward migration reach the peak after midnight (00 LT-03LT). Over both west and east offshore region of Sumatera, the rainfall occurred until 06 LT in the morning. The speed of westward migration is approximately 9 m/s and the eastward migration is approximately 17 m/s.
Figure 4Time-longitude plot of diurnal variation (mm/hour) for period 2000-2010. Averaged over region 2.5⁰S-2.5⁰N. The red dashed lines indicate the coastline of Sumatera.
Modulation of Diurnal Cycle of Rainfall by the MJO
anomalies is larger when the rainfall generally peaked over both land (15 LT) and ocean (21 LT). This indicates that MJO enhances the amplitude of diurnal cycle during its active convection activity (phase 1 to 4)
On the other hand, the diurnal cycle of rainfall tends to be weakened by the MJO from phase 5 to phase 7. During this phase the Indian Ocean is covered by the convectively suppressed area. The negative anomalies are larger over land and ocean when the rainfall generally peaks, indicating that MJO weakens the diurnal cycle of rainfall during convectively suppressed phase (phase 5 to 8). During phase 8, although the negative anomalies of rainfall become smaller, there is no diurnal variation observed. The influence of the MJO on the diurnal cycle of rainfall over Sumatera is consistent with the results of few previous studies. Fujita (2011) suggested that during phase 2 and 3 the amplitude of diurnal cycle of convection were large and there was a clear contrast between the diurnal peaks of convective over land and ocean. Tian et al. (2006) examined the impact of MJO to diurnal cycle during seven winter seasons over maritime continents and found that the diurnal cycle of tropical deep cloud convection was enhanced during the active phase of the MJO, while it was reduced during the inactive phase of the MJO.
The time-longitude plot of diurnal variation of rainfall over the area 2.5⁰ S-2.5⁰N is depicted in Figure 6. The diurnal cycle of rainfall was clearly different at each of MJO phase (Figure 5). The most prominent diurnal cycle and largest amplitude of rainfall was observed during phase 2 and 3. The rainfall over land appears after 09LT, reaches its peak in the afternoon and begins to migrate to the west offshore the island soon after the peak. Chen and Houze (1997) investigated the diurnal cycle of tropical deep convection over the western pacific region during Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They found that the cloud systems are larger during the active phase of MJO and their lifetime is longer; they extend until dawn and decay after sunrise. We can conclude that the peak of rainfall over the ocean changes during this phase. The ocean rainfall reaches the peak twice (21 LT and 3 LT). It is contrary with Tian et al. (2006) which examined that the diurnal phase of deep convective cloud was not affected by the MJO over both land and ocean.
The rainfall anomalies and the fraction of rainfall increase and decrease from the long term mean during each MJO phase is shown in Figure 7. Over land, the positive anomalies of rainfall are larger at 9LT and 15LT and maximum during phase 2 and 3. The anomalies lie between 0.13-0.2 mm/hour (an increase of 33-46% from the long term mean). The negative anomalies are also larger at 9LT and 15LT and reach their maximum during phase 5 and 6. The anomalies lie between -0.12 and -0.18 (decrease by 21-44% to its long term mean). Meanwhile, over ocean the positive anomalies of rainfall are larger at 21 LT and 3 LT and reach maximum during phase 2 and 3. The anomalies lie between 0.09-0.22 mm/hour. Yet this fraction of the long term mean is larger at 9 LT and 15 LT by 26-64% during phase 2 and 3. The negative anomalies of rainfall are larger at 21 LT and 3 LT and reach maximum during phase 6 and 7, which lies between -0.1-(-17) mm/hour. Similar to positive anomalies, the decrease of rainfall to long term mean is larger at 9 LT and 15 LT by 32-54% during phase 6 and 7. Over all, the anomalies and the fraction of rainfall increase and decrease from climatology are larger and more consistent over the ocean than over land because the diurnal variation is small compared to that over the land (high diurnal variation). This is possibly because over the ocean, there is no topography which also play important role in rainfall variation. This is an agreement with Hidayat and Kizu (2009) which suggested that rainfall variability over the ocean is more clearly controlled by the MJO as compared to the large Island.
The Role of Topography Weakens the MJO Signal
The spatial distribution results from the previous section (Figure 5) demonstrate that the rainfall anomalies in association with the MJO were mostly seen in the western side of Sumatera. It indicates that there is heterogeneity of the MJO’s impact on rainfall anomalies between the western side with the eastern side of the mountain range. Previous studies investigated that MJO signal is weakened by complex topography of the maritime continent (Hsu and Lee 2005, Wu and Hsu 2009) and Sumatera is one of the big island with complex topography that possibly responsible for that effects. In order to clarify it, 12 areas are chosen from both western and eastern side of the mountain range of Sumatera. Rainfall anomalies associated with the MJO at 15 LT are displayed in Figure 8. We did not show the diurnal variation since we only were investigating the difference amplitude of rainfall anomalies between the western side and eastern side associated with MJO in no regards to diurnal cycle. Over all selected areas, the anomalies over the western side are larger compared to the anomalies in the eastern side, over both land and ocean. This indicates the impact of MJO on the rainfall over the eastern side is weaker compared to the western side. It also adds evidence to the observation that the topography of Sumatera weakens the signal of MJO when it propagates along the mountainous region.
Figure 7 Composite of rainfall anomalies in each MJO phase (a) over land (b) over ocean. The fraction of decrease and increase of rainfall (c) over land (d) over ocean to climatological mean.
Figure 8 Composite rainfall anomalies at 15 LT in each MJO phase Averaged over area denoted in figure 1.
RegCM4 Performance in Simulating Diurnal Cycle of Rainfall and MJO
The spatial distribution of diurnal cycle of rainfall simulated by RegCM4 for period 2000-2010 is seen in Figure 9. The RegCM4 is able to produce the land-sea contrast of rainfall peak. The rainfall is maximum over land in the morning and minimum in the early morning. While over sea near the coastline, the rainfall reaches maximum in the early morning (3 LT) and minimum in the morning (9LT). The rainfall peaks in the morning (9 LT) which is earlier compared to the TRMM observation. The time interval of the model is every 6-hours (showed in Figure 9), thus it is inconclusive that rainfall peak of the model is actually occurring six hours earlier compared to TRMM.
The error in estimating the time of rainfall peak is possibly caused by the deficiencies of model physics parameterization which related to surface heating such as boundary layer scheme, convective scheme and land surface process scheme. Diro et al. (2012) which carried out the sensitivity experiments to investigate the model sensitivity to land surface found that the bias still present for both the Biospehere-Atmosphere Transfer Scheme (BATS) and CLM land surface scheme. Thus, they suggested that the error probably related to other model physics parameter which involving deep convection scheme. This means that the bias more related to convection scheme which become the convection trigger. In the MIT scheme which was used as the convective scheme in the model, the convection is triggered when the level of buoyancy is higher than the cloud based level which could allow rainfall to generate earlier over land area (Dai & Trenberth 2004). This also causes the over-estimating of light rainfall events. Diro et al. (2012) suggested that this error is common in the global and regional climate model. They also found the similar error when simulating the diurnal cycle of precipitation over Central America using RegCM4.
Figure 9Spatial distribution of rain rate (mm/hour) simulated by RegCM4 for period 2000-2010.
The difference of diurnal variation of rainfall amplitude between TRMM and RegCM4 is pictured in Figure 11. Four points were chosen to be the sample for this purpose, two points are located over land and two located over ocean (the location of four sample points can be seen in Appendix 6). Over land, RegCM4 tended to overestimate the rainfall at 3 LT and 9 LT, but underestimated the rainfall at 15 LT and 21 LT. Meanwhile, the RegCM4 tends to underestimate the rainfall over sea all day long. This could be because the RegCM4 is an atmosphere model, thus the model does not well simulate the rainfall over ocean. In Figure 10, the migration of rainfall towards ocean in the afternoon does not occurred. The rainfall only occurred over land until early night and then dissipates. In order to produce a better result of rainfall over sea, the model must be coupled with an ocean model.
Figure 11 The diurnal variation of rainfall by TRMM and RegCM4 in four sample points.
(a)
(b)
Figure 12 The Taylor diagram displaying the statistical comparison with TRMM of RegCM4 rainfall estimate (mm/hour). (a) Sample over land (A and B) (b) sample over ocean (C and D). The number in each symbol represent the observation time (3 LT, 9 LT, 15 LT and 21 LT).
In addition to testing the model performance in simulating the diurnal cycle of rainfall, we also tested the model performance of diurnal cycle with respect to the MJO events. Figure 13 shows the anomalies of rainfall in each MJO phase after being band-pass filtered. The model simulation captures the positive anomalies of rainfall during the active phase of the MJO (phase 1-4) over the land area, particularly in the morning where the rainfall is at its maximum. The positive anomalies were also observed over land, but with no diurnal variation. Meanwhile, the negative anomalies of rainfall were observed during inactive phase of the MJO (phase 5-8), particularly over the western coast of Sumatera. But the positive anomalies were still observed over ocean during phase 5 and 6. Figure 14 also shows the same pattern with Figure 13 in regards to the MJO, although it does not exhibit the rainfall migration since the rainfall over the ocean is not well simulate by RegCM4. These results indicate that RegCM4 is better at simulating the intra-seasonal cycles such as MJO as compared to the diurnal cycle. Therefore, further research of different model configurations is needed over Sumatera Island to better simulate the diurnal cycle of rainfall in the region.
5 CONCLUSION AND SUGGESTION
Conclusion
This analysis of the diurnal cycle of rainfall confirms results from previous studies suggesting that the diurnal cycle is dominant around Sumatera (Mori et al.
2004, Sakurai et al. 2005, Hamada et al. 2008, Sakurai et al. 2009). The prominent diurnal variation of rainfall over Sumatera was observed by the TRMM satellite data, where there is a clear contrast of diurnal land-sea rainfall. The rainfall is at a maximum over land in the afternoon (15 LT) and a minimum in the early morning. Over the sea, the rainfall maximum occurs at night (21 LT) and a minimum in the afternoon. This diurnal variation is consistent with previous studies conducted in the tropical region which suggest that the rainfall peaks in the afternoon over land and in the night time or early morning over sea (Nitta and Sekine 1994, Chen and Takahashi 1995, Ohsawa et al. 2001). The MJO influences the diurnal cycle of rainfall by modulating the amplitude of rainfall and the rainfall peak over the ocean. During its active convective phase (phase 2 and 3), the MJO increase the rainfall over land and ocean with fraction of 22-46 % and 26-64% to its long term mean, respectively. The diurnal variation becomes more prominent because the anomalies are larger when the rainfall reaches maximum over both land and sea. On the other hand, the MJO reduces the rainfall during its inactive phase (phase 5 and 6) over land and ocean with fraction of 21-44% and 32-54% to its long term mean, respectively. The rainfall peak over the ocean occurs twice during phase 2 (18 LT and 00 LT) and phase 3 (21 LT and 3 LT), while during inactive phase rainfall over the ocean never occurred.
The RegCM4 that we employed to simulate the diurnal cycle of rainfall was able to produce the diurnal variation of the rainfall (land-sea rainfall contrast), but unable to capture the peak time of rainfall over the land. Diro et al (2012) suggested that the error is a common problem to many global and regional climate models. The error of the model is larger when the rainfall is maximum indicating that the bias of the model possibly is related to the physics of parameterization which involves the convection scheme. The model is able to depict the positive anomalies during the active phase of MJO and negative anomalies during inactive phase of MJO, particularly over land. We suggest that the MJO is better in simulating the intra-seasonal cycle such the MJO than the diurnal cycle.
Suggestion
Further research of individual MJO impacts on diurnal cycle can be conducted to investigate the influence of the strength of MJO signal on rainfall variability. Moreover, in order to get the good performance of RegCM4 model in simulating the diurnal cycle of rainfall over Sumatera Island, the study with different RegCM4 configurations (model parameterization) can be carried out.
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APPENDICES
1. TRMM V6 3B42 Precipitation Product Algorithm
3. Roadmap of MJO
4. Research Flowchart
5. The Sample of Filtered Data Anomaly Compared to Daily Mean Anomaly (a) Filtered data (20-90 days cut off frequency)
Year (b) Anomaly from daily mean
Year
The rainfall anomalies after being filtered by Lanzsos filter is depicted in Figure (a). It was band-pass filtered with cut off frequency 20-90 days. Thus, the rainfall anomalies were only caused by the MJO. The other frequencies that also influence rainfall variability such as Indian Ocean dipole and El-Nino Southern oscillation were eliminated. Meanwhile Figure (b) shows the anomaly from daily mean.
R
ainfa
ll
a
nomal
y
(
mm
/hr)
R
ainfa
ll
a
nomal
y
(
mm
6. The location of four samples points in Figure 11
The details of coordinate of each point can be seen in the table below:
Location Latitude (°) Longitude (°)
A -0.2 100.3
B 1.4 103.4
C -2.5 97.1
D -0.1 105.9
L
ati
tude
(º
)
7. The Correlation, RMSE and ratio of Variance showed in Taylor Diagrams at four sample points around Sumatera
Tabel 1 The correlation value
Point (lat, lon) 3 LT 9 LT 15 LT 21 LT
A (-0.2, 100.3) 0.02 0.10 0.05 0.00
B (1.4, 103.4) 0.46 0.42 0.16 0.18
C (-2.5, 97.1) 0.01 0.01 0.01 0.10
D (-0.1, 105.9) 0.03 0.03 0.04 0.05
Table 2 RMSE
Point (lat, lon) 03 LT 09 LT 15 LT 21 LT
A (-0.2, 100.3) 1.46 2.40 1.65 0.86
B (1.4, 103.4) 0.98 2.28 1.43 1.51
C (-2.5, 97.1) 1.33 1.13 0.92 1.05
D (-0.1, 105.9) 1.62 1.04 0.91 1.35
Table 3 Ratio of Varians
Point (lat, lon) 3 LT 9 LT 15 LT 21 LT
A (-0.2, 100.3) 8.50 1.55 0.35 0.41
B (1.4, 103.4) 0.46 0.42 0.16 0.18
C (-2.5, 97.1) 0.17 0.10 0.12 0.09
BIOGRAPHY
Rahmi Ariani was born in Solok on January 3th, 1989 as the only daughter of Syafri Marah and Rifda. She is the sixth child among six siblings. Rahmi hold a bachelor degree from Department of Geophysics and Meteorology, IPB in 2011 and at the same year pursued her master degree in Graduate school IPB and majoring Applied Climatology.
