Interdecadal Variability in Myanmar Rainfall in the Monsoon Season (May–October) Using Eigen Methods
Abstract
:1. Introduction
2. Study Area
3. Data and Methodology
3.1. Data
3.2. Methods
4. Results
4.1. General Characteristics of Monsoon Rainfall
4.1.1. Annual Cycle
4.1.2. Climatology of Summer Monsoon Rainfall
4.2. Spatial and Temporal Interdecadal Variability in Summer Monsoon Rainfall.
4.3. Interdecadal Variations in Rainfall and Associated Circulation Influences
4.3.1. Interdecadal Rainfall Variability
4.3.2. Large-Scale Atmospheric Circulation Pattern
4.3.3. Role of Oceans
5. Discussion
6. Conclusions
- (1)
- The interdecadal variation over the target region increased in the late 1950s, reaching its peak around 1965, and subsequently decreased in the mid-1980s before increasing again in the early 1990s. An abrupt rainfall shift was recorded during the 1970s and a significant reduction (at the 95% confidence level) in rainfall was evident between the 1980s and the 2000s.
- (2)
- In terms of rainfall trend, the regional variability in monsoon rainfall over Myanmar was obvious during the 1950–2019 period. The magnitude of the rainfall variability could possibly be associated with the geographical location of the region and monsoon circulation patterns. The temporal trend over the Asian and Indian monsoon systems showed strong variation on different time scales, including monthly, inter-annual, intra-seasonal, and annual time scales. Such large-scale variation caused by different factors could induce changes in monsoon rainfall over the diverse study regions.
- (3)
- The widely used EOF method showed that the interdecadal rainfall pattern over Myanmar significantly changed over the study period, especially during EOF2 and EOF3. PC1 and PC2 experienced an increasing trend over the target region during the period 1950–2019.
- (4)
- The results further demonstrate the significant negative westerly and southwesterly wind anomalies from the BoB and Andaman Sea over almost the whole region except for the eastern and southern regions during wet years. The moisture transport at 850 hPa revealed that the anomalous moisture convergence (positive anomalies) emerged in the central and northern region, transporting moisture from the Western BoB and Eastern Gulf of Thailand. This cyclonic circulation may cause more water vapor from the BoB and Andaman Sea to be transported into the country. In addition, warm or positive PDO phase was associated with low rainfall in Myanmar, and cooling or negative PDO phase can increase rainfall over Myanmar. The AMO index had a mean average value of 0.53 for the same period. In addition, the effects of excessive rainfall occurred in the negative stages of the PDO, and no serious adverse events occurred in the positive phase of the PDO. The first SVD of SST mode 1 was mainly linked to the PDO cold phase in the Pacific Ocean above the North Pacific Ocean (20° N) and the warm AMO phase in the North Atlantic Ocean.
- (5)
- The SST patterns showed that the warm SST of the eastern equatorial Pacific SST is a pattern of the El Niño SST in the Pacific Ocean and the cold SST in the North Atlantic Ocean. The fractional covariance fraction of SVD2 was recorded as 33%, whereas the coefficient of correlation between the coefficients for the two-phase period was 0.94. Therefore, we concluded that the findings of the present study provide valuable results regarding the interdecadal variability in Myanmar summer monsoon rainfall, which is shown as a negative correlation with the PDO and a positive association with the AMO. In addition, Myanmar’s decadal summer monsoon rainfall and relationships with the PDO and AMO indexes explain the flood and dry conditions (drought) in the region.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Goswami, B.N.; Madhusoodanan, M.S.; Neema, C.P.; Sengupta, D. A physical mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophys. Res. Lett. 2006, 33. [Google Scholar] [CrossRef] [Green Version]
- Saleem, F.; Zeng, X.; Hina, S.; Omer, A. Regional changes in extreme temperature records over Pakistan and their relation to Pacific variability. Atmos. Res. 2021, 250, 105407. [Google Scholar] [CrossRef]
- Hina, S.; Saleem, F. Historical analysis (1981–2017) of drought severity and magnitude over a predominantly arid region of Pakistan. Clim. Res. 2019, 78, 189–204. [Google Scholar] [CrossRef]
- Suman, M.; Maity, R. Southward shift of precipitation extremes over south Asia: Evidences from CORDEX data. Sci. Rep. 2020, 10, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Ren, Y.-Y.; Ren, G.-Y.; Sun, X.-B.; Shrestha, A.B.; You, Q.-L.; Zhan, Y.-J.; Rajbhandari, R.; Zhang, P.-F.; Wen, K.-M. Observed changes in surface air temperature and precipitation in the Hindu Kush Himalayan region over the last 100-plus years. Adv. Clim. Chang. Res. 2017, 8, 148–156. [Google Scholar] [CrossRef]
- Lone, S.A.; Jeelani, G.; Deshpande, R.D.; Mukherjee, A. Stable isotope (δ18O and δD) dynamics of precipitation in a high altitude Himalayan cold desert and its surroundings in Indus river basin, Ladakh. Atmos. Res. 2019, 221, 46–57. [Google Scholar] [CrossRef]
- Zhi, X.; Tian, X.; Liu, P.; Hu, Y. Interdecadal variations in winter extratropical anticyclones in East Asia and their impacts on the decadal mode of East Asian surface air temperature. Theor. Appl. Clim. 2019, 131, 1763–1775. [Google Scholar] [CrossRef]
- Hou, M.; Duan, W.; Zhi, X. Season-dependent predictability barrier for two types of El Niño revealed by an approach to data analysis for predictability. Clim. Dyn. 2019, 53, 5561–5581. [Google Scholar] [CrossRef] [Green Version]
- Tangang, F.; Chung, J.X.; Juneng, L.; Supari; Salimun, E.; Ngai, S.T.; Jamaluddin, A.F.; Mohd, M.S.F.; Cruz, F.; Narisma, G.; et al. Projected future changes in rainfall in Southeast Asia based on CORDEX–SEA multi-model simulations. Clim. Dyn. 2020, 55, 1247–1267. [Google Scholar] [CrossRef]
- Almazroui, M.; Saeed, S.; Saeed, F.; Islam, M.N.; Ismail, M. Projections of Precipitation and Temperature over the South Asian Countries in CMIP6. Earth Syst. Environ. 2020, 4, 297–320. [Google Scholar] [CrossRef]
- Ge, F.; Zhu, S.; Luo, H.; Zhi, X.; Wang, H. Future changes in precipitation extremes over Southeast Asia: Insights from CMIP6 multi-model ensemble. Environ. Res. Lett. 2021, 16, 024013. [Google Scholar] [CrossRef]
- Song, B.; Zhi, X.; Pan, M.; Hou, M.; He, C.; Fraedrich, K. Turbulent Heat Flux Reconstruction in the North Pacific from 1921 to 2014. J. Meteorol. Soc. Jpn. 2019, 97, 893–911. [Google Scholar] [CrossRef] [Green Version]
- Francis, R.C.; Hare, S.R. Decadal-scale regime shifts in the large marine ecosystems of the North-east Pacific: A case for historical science. Fish. Oceanogr. 1994, 3, 279–291. [Google Scholar] [CrossRef]
- Latif, M.; Barnett, T.P. Causes of Decadal Climate Variability over the North Pacific and North America. Science 1994, 266, 634–637. [Google Scholar] [CrossRef] [Green Version]
- Yang, Y.; Gan, T.Y.; Tan, X. Spatiotemporal Changes in Precipitation Extremes over Canada and Their Teleconnections to Large-Scale Climate Patterns. J. Hydrometeorol. 2019, 20, 275–296. [Google Scholar] [CrossRef]
- Ge, F.; Zhu, S.; Peng, T.; Zhao, Y.; Sielmann, F.; Fraedrich, K.; Zhi, X.; Liu, X.; Tang, W.; Ji, L. Risks of precipitation extremes over Southeast Asia: Does 1.5 °C or 2 °C global warming make a difference? Environ. Res. Lett. 2019, 14, 044015. [Google Scholar] [CrossRef]
- Ge, F.; Zhi, X.; Babar, Z.A.; Tang, W.; Chen, P. Interannual variability of summer monsoon precipitation over the Indochina Peninsula in association with ENSO. Theor. Appl. Clim. 2016, 128, 523–531. [Google Scholar] [CrossRef]
- Liu, B.; Wu, G.; Ren, R. Influences of ENSO on the vertical coupling of atmospheric circulation during the onset of South Asian summer monsoon. Clim. Dyn. 2014, 45, 1859–1875. [Google Scholar] [CrossRef]
- Xu, J.; Koldunov, N.V.; Remedio, A.R.C.; Sein, D.V.; Rechid, D.; Zhi, X.; Jiang, X.; Xu, M.; Zhu, X.; Fraedrich, K.; et al. Downstream effect of Hengduan Mountains on East China in the REMO regional climate model. Theor. Appl. Clim. 2018, 135, 1641–1658. [Google Scholar] [CrossRef]
- Zhang, L.; Zhu, X.; Fraedrich, K.; Sielmann, F.; Zhi, X. Interdecadal variability of winter precipitation in Southeast China. Clim. Dyn. 2014, 43, 2239–2248. [Google Scholar] [CrossRef] [Green Version]
- Zheng, Y.; Zhang, Q.; Luo, M.; Sun, P.; Singh, V.P. Wintertime precipitation in eastern China and relation to the Madden-Julian oscillation: Spatiotemporal properties, impacts and causes. J. Hydrol. 2020, 582, 124477. [Google Scholar] [CrossRef]
- Mantua, N.J.; Hare, S.R.; Zhang, Y.; Wallace, J.M.; Francis, R.C. A Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production. Bull. Am. Meteorol. Soc. 1997, 1069–1079. [Google Scholar] [CrossRef]
- Zhang, L.; Sielmann, F.; Fraedrich, K.; Zhu, X.; Zhi, X. Variability of winter extreme precipitation in Southeast China: Contributions of SST anomalies. Clim. Dyn. 2015, 45, 2557–2570. [Google Scholar] [CrossRef]
- Chhin, R.; Shwe, M.M.; Yoden, S. Time-lagged correlations associated with interannual variations of pre-monsoon and post-monsoon precipitation in Myanmar and the Indochina Peninsula. Int. J. Clim. 2020, 40, 3792–3812. [Google Scholar] [CrossRef]
- Sein, Z.M.M.; Islam, A.R.M.T.; Maw, K.W.; Moya, T.B. Characterization of southwest monsoon onset over Myanmar. Theor. Appl. Clim. 2015, 127, 587–603. [Google Scholar] [CrossRef]
- Burki, T. Floods in Myanmar damage hundreds of health facilities. Lancet 2015, 386, 843. [Google Scholar] [CrossRef]
- Chen, F.-H.; Huang, W. Multi-scale climate variations in the arid Central Asia. Adv. Clim. Chang. Res. 2017, 8, 1–2. [Google Scholar] [CrossRef]
- Zaw, Z.; Fan, Z.-X.; Bräuning, A.; Liu, W.; Gaire, N.P.; Than, K.Z.; Panthi, S. Monsoon precipitation variations in Myanmar since AD 1770: Linkage to tropical ocean-atmospheric circulations. Clim. Dyn. 2021, 1–16. [Google Scholar] [CrossRef]
- Ge, F.; Peng, T.; Fraedrich, K.; Sielmann, F.; Zhu, X.; Zhi, X.; Liu, X.; Tang, W.; Zhao, P. Assessment of trends and variability in surface air temperature on multiple high-resolution datasets over the Indochina Peninsula. Theor. Appl. Clim. 2019, 135, 1609–1627. [Google Scholar] [CrossRef]
- Xu, J.; Koldunov, N.; Remedio, A.R.C.; Sein, D.V.; Zhi, X.; Jiang, X.; Xu, M.; Zhu, X.; Fraedrich, K.; Jacob, D. On the role of horizontal resolution over the Tibetan Plateau in the REMO regional climate model. Clim. Dyn. 2018, 51, 4525–4542. [Google Scholar] [CrossRef] [Green Version]
- Kreft, S.; Eckstein, D. Global Climate Risk Index 2014: Who suffers most from extreme weather events? Weather-related loss events in 2012 and 1993 to 2012. Ger. Brief. Pap. 2013, 28. [Google Scholar]
- Zhang, L.; Sielmann, F.; Fraedrich, K.; Zhi, X. Atmospheric response to Indian Ocean Dipole forcing: Changes of Southeast China winter precipitation under global warming. Clim. Dyn. 2016, 48, 1467–1482. [Google Scholar] [CrossRef]
- Sen Roy, N.; Kaur, S. Climatology of monsoon rains of Myanmar (Burma). Int. J. Climatol. 2000, 913–928. [Google Scholar] [CrossRef]
- Zaw, Z.; Fan, Z.; Bräuning, A.; Xu, C.; Liu, W.; Gaire, N.P.; Panthi, S.; Than, K.Z. Drought Reconstruction Over the Past Two Centuries in Southern Myanmar Using Teak Tree-Rings: Linkages to the Pacific and Indian Oceans. Geophys. Res. Lett. 2020, 47. [Google Scholar] [CrossRef]
- Zhang, L.; Zhi, X.F. Multimodel consensus forecasting of low temperature and icy weather over central and Southern China in early 2008. J. Trop. Meteorol. 2015, 67–75. [Google Scholar] [CrossRef]
- Sein, M.M.Z.; Ogwang, B.A.; Ongoma, V.; Ogou, F.K.; Batebana, K. Inter-annual variability of Summer Monsoon Rainfall over Myanmar in relation to IOD and ENSO. J. Environ. Agric. Sci. 2015, 4, 28–36. [Google Scholar]
- Oo, S.S.; Hmwe, K.M.; Aung, N.N.; Su, A.A.; Soe, K.K.; Mon, T.L.; Lwin, K.M.; Thu, M.M.; Soe, T.T.; Htwe, M.L. Diversity of Insect Pest and Predator Species in Monsoon and Summer Rice Fields of Taungoo Environs, Myanmar. Adv. Èntomol. 2020, 8, 117–129. [Google Scholar] [CrossRef]
- Omer, A.; Elagib, N.A.; Zhuguo, M.; Saleem, F.; Mohammed, A. Water scarcity in the Yellow River Basin under future climate change and human activities. Sci. Total. Environ. 2020, 749, 141446. [Google Scholar] [CrossRef]
- Schneider, U.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Ziese, M.; Rudolf, B. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Clim. 2014, 115, 15–40. [Google Scholar] [CrossRef] [Green Version]
- Aadhar, S.; Mishra, V. A substantial rise in the area and population affected by dryness in South Asia under 1.5 °C, 2.0 °C and 2.5 °C warmer worlds. Environ. Res. Lett. 2019, 14, 114021. [Google Scholar] [CrossRef]
- Ahmed, K.; Shahid, S.; Sachindra, D.; Nawaz, N.; Chung, E.-S. Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method. J. Hydrol. 2019, 573, 281–298. [Google Scholar] [CrossRef]
- Onyutha, C. Analyses of rainfall extremes in East Africa based on observations from rain gauges and climate change simulations by CORDEX RCMs. Clim. Dyn. 2020, 54, 4841–4864. [Google Scholar] [CrossRef]
- Liu, Y.B.; Song, P.; Peng, J.; Fu, Q.N.; Dou, C.C. Recent increased frequency of drought events in Poyang Lake Basin, China: Climate change or anthropogenic effects? Hydro-Climatol. Var. Chang. 2011, 344, 99–104. [Google Scholar]
- Khan, N.; Sachindra, D.; Shahid, S.; Ahmed, K.; Shiru, M.S.; Nawaz, N. Prediction of droughts over Pakistan using machine learning algorithms. Adv. Water Resour. 2020, 139, 103562. [Google Scholar] [CrossRef]
- Zhang, T.; Lin, X. Assessing future drought impacts on yields based on historical irrigation reaction to drought for four major crops in Kansas. Sci. Total. Environ. 2016, 550, 851–860. [Google Scholar] [CrossRef] [Green Version]
- Knight, J.H.; Minasny, B.; McBratney, A.B.; Koen, T.B.; Murphy, B.W. Soil temperature increase in eastern Australia for the past 50 years. Geoderma 2018, 313, 241–249. [Google Scholar] [CrossRef]
- Rayner, N.A.; Parker, D.E.; Horton, E.B.; Folland, C.K.; Alexander, L.V.; Rowell, D.P.; Kent, E.C.; Kaplan, A.L. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Space Phys. 2003, 108. [Google Scholar] [CrossRef]
- Hersbach, H.; Bell, B.; Berrisford, P.; Horányi, A.; Sabater, J.M.; Nicolas, J.; Radu, R.; Schepers, D.; Simmons, A.; Soci, C.; et al. Global reanalysis: Goodbye ERA-Interim, hello ERA5. ECMWF Newsl. 2019, 17–24. [Google Scholar] [CrossRef]
- Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
- Smith, T.M.; Reynolds, R.W.; Peterson, T.C.; Lawrimore, J. Improvements to NOAA’s Historical Merged Land–Ocean Surface Temperature Analysis (1880–2006). J. Clim. 2008, 21, 2283–2296. [Google Scholar] [CrossRef]
- Kalnay, E.; Kanamitsu, M.; Kistler, R.; Collins, W.; Deaven, D.; Gandin, L.; Iredell, M.; Saha, S.; White, G.; Woollen, J.; et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 1996, 37–472. [Google Scholar] [CrossRef] [Green Version]
- Mardia, K.V. Multi-dimensional multivariate Gaussian Markov random fields with application to image processing. J. Multivar. Anal. 1988, 24, 265–284. [Google Scholar] [CrossRef] [Green Version]
- Hannachi, A.; Jolliffe, I.T.; Stephenson, D.B. Empirical orthogonal functions and related techniques in atmospheric science: A review. Int. J. Clim. 2007, 27, 1119–1152. [Google Scholar] [CrossRef]
- Levine, R.A.; Wilks, D.S. Statistical Methods in the Atmospheric Sciences. J. Am. Stat. Assoc. 2000, 95, 344. [Google Scholar] [CrossRef]
- Walsh, J.E.; Mostek, A. A Quantitative Analysis of Meteorological Anomaly Patterns Over the United States, 1900–1977. Mon. Weather. Rev. 1980, 108, 615–630. [Google Scholar] [CrossRef]
- Smakhtin, V.U.; Hughes, D.A. Automated estimation and analyses of meteorological drought characteristics from monthly rainfall data. Environ. Model. Softw. 2007, 22, 880–890. [Google Scholar] [CrossRef]
- Gilman, D.L.; Fuglister, F.J.; Mitchell, J.M. On the Power Spectrum of “Red Noise”. J. Atmos. Sci. 1963, 182–184. [Google Scholar] [CrossRef] [Green Version]
- Mitchell, J.M. Further Remarks on the Power Spectrum of “Red Noise”. J. Atmos. Sci. 1964, 461. [Google Scholar] [CrossRef] [Green Version]
- Joshi, M.K.; Pandey, A.C. Trend and spectral analysis of rainfall over India during 1901–2000. J. Geophys. Res. Space Phys. 2011, 116. [Google Scholar] [CrossRef]
- Zhu, S.; Ge, F.; Fan, Y.; Zhang, L.; Sielmann, F.; Fraedrich, K.; Zhi, X. Conspicuous temperature extremes over Southeast Asia: Seasonal variations under 1.5 °C and 2 °C global warming. Clim. Chang. 2020, 160, 343–360. [Google Scholar] [CrossRef]
- Folland, C.K.; Knight, J.; Linderholm, H.W.; Fereday, D.; Ineson, S.; Hurrell, J.W. The Summer North Atlantic Oscillation: Past, Present, and Future. J. Clim. 2009, 22, 1082–1103. [Google Scholar] [CrossRef]
- Wen, Z.; Niu, F.; Yu, Q.; Wang, D.; Feng, W.; Zheng, J. The role of rainfall in the thermal-moisture dynamics of the active layer at Beiluhe of Qinghai-Tibetan plateau. Environ. Earth Sci. 2013, 71, 1195–1204. [Google Scholar] [CrossRef]
- Banacos, P.C.; Schultz, D.M. The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives. Weather Forecast. 2005, 20, 351–366. [Google Scholar] [CrossRef]
- Mann, H.B. Nonparametric Tests against Trend. Econometrica 1945, 13, 245. [Google Scholar] [CrossRef]
- Kendall, M.G. Rank Correlation Methods; Griffin: London, UK, 1975; ISBN 0852641990. [Google Scholar]
- Ghosh, K.G. Spatial and temporal appraisal of drought jeopardy over the Gangetic West Bengal, eastern India. Geoenvironmental Disasters 2019, 6, 1. [Google Scholar] [CrossRef] [Green Version]
- Waseem, M.; Ahmad, I.; Mujtaba, A.; Tayyab, M.; Si, C.; Lü, H.; Dong, X. Spatiotemporal Dynamics of Precipitation in Southwest Arid-Agriculture Zones of Pakistan. Sustainability 2020, 12, 2305. [Google Scholar] [CrossRef] [Green Version]
- Payab, A.H.; Türker, U. Comparison of standardized meteorological indices for drought monitoring at northern part of Cyprus. Environ. Earth Sci. 2019, 78, 309. [Google Scholar] [CrossRef]
- Naz, F.; Dars, G.H.; Ansari, K.; Jamro, S.; Krakauer, N.Y. Drought Trends in Balochistan. Water 2020, 12, 470. [Google Scholar] [CrossRef] [Green Version]
- Musonda, B.; Jing, Y.; Iyakaremye, V.; Ojara, M. Analysis of Long-Term Variations of Drought Characteristics Using Standardized Precipitation Index over Zambia. Atmosphere 2020, 11, 1268. [Google Scholar] [CrossRef]
- Wang, F.; Yang, H.; Wang, Z.; Zhang, Z.; Li, Z. Drought Evaluation with CMORPH Satellite Precipitation Data in the Yellow River Basin by Using Gridded Standardized Precipitation Evapotranspiration Index. Remote. Sens. 2019, 11, 485. [Google Scholar] [CrossRef] [Green Version]
- Almazroui, M.; Şen, Z. Trend Analyses Methodologies in Hydro-meteorological Records. Earth Syst. Environ. 2020, 4, 713–738. [Google Scholar] [CrossRef]
- Ahmed, K.; Shahid, S.; Wang, X.; Nawaz, N.; Khan, N. Spatiotemporal changes in aridity of Pakistan during 1901–2016. Hydrol. Earth Syst. Sci. 2019, 23, 3081–3096. [Google Scholar] [CrossRef] [Green Version]
- Rahman, G.; Dawood, M. Spatial and temporal variation of rainfall and drought in Khyber Pakhtunkhwa Province of Pakistan during 1971–2015. Arab. J. Geosci. 2018, 11, 46. [Google Scholar] [CrossRef]
- Bretherton, C.S.; Smith, C.; Wallace, J.M. An Intercomparison of Methods for Finding Coupled Patterns in Climate Data. J. Clim. 1992, 541–560. [Google Scholar] [CrossRef] [Green Version]
- Wallace, J.M.; Smith, C.; Bretherton, C.S. Singular Value Decomposition of Wintertime Sea Surface Temperature and 500-mb Height Anomalies. J. Clim. 1992, 561–576. [Google Scholar] [CrossRef]
- Wu, Z.; Huang, N.E.; Long, S.R.; Peng, C.-K. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl. Acad. Sci. USA 2007, 104, 14889–14894. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wu, Z.; Huang, N.E. Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method. Adv. Adapt. Data Anal. 2009, 1, 1–41. [Google Scholar] [CrossRef]
- Zhu, S.; Remedio, A.R.C.; Sein, D.V.; Sielmann, F.; Ge, F.; Xu, J.; Peng, T.; Jacob, D.; Fraedrich, K.; Zhi, X. Added value of the regionally coupled model ROM in the East Asian summer monsoon modeling. Theor. Appl. Clim. 2020, 140, 375–387. [Google Scholar] [CrossRef] [Green Version]
- Wu, J.; Zhou, L.; Mo, X.; Zhou, H.; Zhang, J.; Jia, R. Drought monitoring and analysis in China based on the Integrated Surface Drought Index (ISDI). Int. J. Appl. Earth Obs. Geoinf. 2015, 41, 23–33. [Google Scholar] [CrossRef]
- Roy, S.S.; Roy, N.S. Influence of Pacific decadal oscillation and El Niño Southern oscillation on the summer monsoon precipitation in Myanmar. Int. J. Clim. 2010, 31, 14–21. [Google Scholar] [CrossRef]
- Kumar, K.K.; Rajagopalan, B.; Hoerling, M.; Bates, G.; Cane, M. Unraveling the Mystery of Indian Monsoon Failure during El Niño. Science 2006, 314, 115–119. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kreft, S.; Eckstein, D. Global Climate Risk Index 2014. Who Suffers Most from Extreme Weather Events? Germanwatch: Bonn, Germany, 2016; ISBN 9783943704143. [Google Scholar]
- Delworth, T.L.; Mann, M.E. Observed and simulated multidecadal variability in the Northern Hemisphere. Clim. Dyn. 2000, 16, 661–676. [Google Scholar] [CrossRef] [Green Version]
- Kerr, R.A. A North Atlantic Climate Pacemaker for the Centuries. Science 2000, 288, 1984–1985. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bowling, L.C.; Lettenmaier, D.P.; Nijssen, B.; Graham, L.P.; Clark, D.B.; El Maayar, M.; Essery, R.; Goers, S.; Gusev, Y.M.; Habets, F.; et al. Simulation of high-latitude hydrological processes in the Torne–Kalix basin: PILPS Phase 2(e). Glob. Planet. Chang. 2003, 38, 1–30. [Google Scholar] [CrossRef] [Green Version]
- Chang, C.-P.; Zhang, Y.; Li, T. Interannual and Interdecadal Variations of the East Asian Summer Monsoon and Tropical Pacific SSTs. Part I: Roles of the Subtropical Ridge. J. Clim. 2000, 13, 4310–4325. [Google Scholar] [CrossRef] [Green Version]
- Krishnan, R.; Sugi, M. Pacific decadal oscillation and variability of the Indian summer monsoon rainfall. Clim. Dyn. 2003, 21, 233–242. [Google Scholar] [CrossRef]
- Zhu, S.; Ge, F.; Sielmann, F.; Pan, M.; Fraedrich, K.; Remedio, A.R.C.; Sein, D.V.; Jacob, D.; Wang, H.; Zhi, X. Seasonal temperature response over the Indochina Peninsula to a worst-case high-emission forcing: A study with the regionally coupled model ROM. Theor. Appl. Clim. 2020, 142, 613–622. [Google Scholar] [CrossRef]
- Webster, P.J.; Yang, S. Monsoon and Enso: Selectively Interactive Systems. Q. J. R. Meteorol. Soc. 1992, 118, 877–926. [Google Scholar] [CrossRef]
- Reckien, D.; Petkova, E.P. Who is responsible for climate change adaptation? Environ. Res. Lett. 2018, 14, 014010. [Google Scholar] [CrossRef]
- Supari; Tangang, F.; Juneng, L.; Cruz, F.; Chung, J.X.; Ngai, S.T.; Salimun, E.; Mohd, M.S.F.; Santisirisomboon, J.; Singhruck, P.; et al. Multi-model projections of precipitation extremes in Southeast Asia based on CORDEX-Southeast Asia simulations. Environ. Res. 2020, 184, 109350. [Google Scholar] [CrossRef]
Month | In Situ | GPCC (mm) | CRUTS4.0 (mm) |
---|---|---|---|
Jan | 4.66 | 5.28 | 5.60 |
Feb | 9.94 | 10.54 | 12.12 |
Mar | 20.93 | 20.19 | 20.72 |
Apr | 55.25 | 53.95 | 62.75 |
May | 248.03 | 237.92 | 224.41 |
Jun | 480.32 | 478.80 | 444.51 |
Jul | 520.03 | 519.32 | 466.11 |
Aug | 524.53 | 519.38 | 475.89 |
Sep | 340.86 | 344.49 | 328.81 |
Oct | 175.52 | 179.23 | 190.30 |
Nov | 53.25 | 57.80 | 64.77 |
Dec | 9.10 | 9.36 | 9.54 |
Dataset | Correlation Coefficient (R) | RMSE |
---|---|---|
GPCC | 0.94 | 14.24 |
CRUTS4.0 | 0.52 | 45.29 |
Sr. No | Year | Station | Region/State | Rainfall (mm) |
---|---|---|---|---|
1 | 4.9.1965 | Kyaukpyu | Rakhine | 568 |
2 | 24.8.1997 | Dawei | Tanintharyi | 549 |
3 | 29.6.1989 | Hkamti | Upper Sagaing | 527 |
4 | 4.7.2006 | Dawei | Tanintharyi | 447 |
5 | 5.6.1980 | Sittwe | Rakhine | 422 |
6 | 18.7.1956 | Thandwe | Rakhine | 353 |
Indices | Interannual Component | Interdecadal Component |
---|---|---|
NP | –0.24 | –0.17 |
NAO | –0.38 * | –0.15 |
AO | –0.02 | –0.21 |
PDO | –0.39 * | –0.79 * |
AMO | 0.53 * | 0.54 * |
Mode | Squared Covariance | Temporal Correlation | SST Variance | Rainfall Variance |
---|---|---|---|---|
Mode 1 | 53% | 0.94 | 47% | 20% |
Mode 2 | 33% | 0.94 | 21% | 28% |
Mode 3 | 6.7% | 0.93 | 9% | 13% |
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Mie Sein, Z.M.; Ullah, I.; Saleem, F.; Zhi, X.; Syed, S.; Azam, K. Interdecadal Variability in Myanmar Rainfall in the Monsoon Season (May–October) Using Eigen Methods. Water 2021, 13, 729. https://doi.org/10.3390/w13050729
Mie Sein ZM, Ullah I, Saleem F, Zhi X, Syed S, Azam K. Interdecadal Variability in Myanmar Rainfall in the Monsoon Season (May–October) Using Eigen Methods. Water. 2021; 13(5):729. https://doi.org/10.3390/w13050729
Chicago/Turabian StyleMie Sein, Zin Mie, Irfan Ullah, Farhan Saleem, Xiefei Zhi, Sidra Syed, and Kamran Azam. 2021. "Interdecadal Variability in Myanmar Rainfall in the Monsoon Season (May–October) Using Eigen Methods" Water 13, no. 5: 729. https://doi.org/10.3390/w13050729