Unravelling the teleconnections between ENSO and dry/wet conditions over India using nonlinear Granger causality

Atmospheric Research - Tập 247 - Trang 105168 - 2021
Vivek Gupta1, Manoj Kumar Jain1
1Department of Hydrology, Indian Institute of Technology, Roorkee, India

Tài liệu tham khảo

Asimakopoulos, 2000, Nonlinear Granger causality in the currency futures returns, Econ. Lett., 68, 25, 10.1016/S0165-1765(00)00219-6 Attanasio, 2011, Detecting human influence on climate using neural networks based Granger causality, Theor. Appl. Climatol., 103, 103, 10.1007/s00704-010-0285-8 Bayissa, 2018, Comparison of the performance of six drought indices in characterizing historical drought for the upper Blue Nile Basin, Ethiopia, Geosci. (Switzerland), 8, 81 Beguería, 2014, Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring, Int. J. Climatol., 34, 3001, 10.1002/joc.3887 Belayneh, 2014, Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models, J. Hydrol., 508, 418, 10.1016/j.jhydrol.2013.10.052 Bisht, 2019, Drought characterization over India under projected climate scenario, Int. J. Climatol., 39, 1889, 10.1002/joc.5922 Bonsal, 2017, An assessment of historical and projected future hydro-climatic variability and extremes over southern watersheds in the Canadian Prairies, Int J Climatol, 37, 3934, 10.1002/joc.4967 Bruns, 2019, Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models, Empir. Econ., 56, 797, 10.1007/s00181-018-1446-3 Cai, 2014, Increasing frequency of extreme El Niño events due to greenhouse warming, Nat. Clim. Chang., 4, 111, 10.1038/nclimate2100 Cai, 2015, ENSO and greenhouse warming, Nat. Clim. Chang., 5, 849, 10.1038/nclimate2743 Chen, 2015, Changes in drought characteristics over China using the standardized precipitation evapotranspiration index, J. Clim., 28, 5430, 10.1175/JCLI-D-14-00707.1 Chen, 2017, Characterizing present and future drought changes over eastern China, Int J Climatol, 37, 138, 10.1002/joc.4987 Chiou-Wei, 2008, Economic growth and energy consumption revisited - evidence from linear and nonlinear Granger causality, Energy Econ., 30, 3063, 10.1016/j.eneco.2008.02.002 DeFlorio, 2013, Western U.S. extreme precipitation events and their relation to ENSO and PDO in CCSM4, J. Clim., 26, 4231, 10.1175/JCLI-D-12-00257.1 Dibike, 2017, Implications of future climate on water availability in the western Canadian river basins, Int J Climatol, 37, 3247, 10.1002/joc.4912 Dutta, 2018, Temporal evolution of hydroclimatic teleconnection and a time-varying model for long-lead prediction of Indian summer monsoon rainfall, Sci. Rep., 8, 10.1038/s41598-018-28972-z Dutta, 2015, Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI), Egypt. J. Remote Sens. Space Sci., 18, 53 Feng, 2017, Why do different drought indices show distinct future drought risk outcomes in the U.S Great plains?, J Clim, 30, 265, 10.1175/JCLI-D-15-0590.1 Forootan, 2016, Quantifying the impacts of ENSO and IOD on rain gauge and remotely sensed precipitation products over Australia, Remote Sens. Environ., 172, 50, 10.1016/j.rse.2015.10.027 Galla, 2018 Ganguli, 2014, Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach, Hydrol. Process., 28, 4989, 10.1002/hyp.9966 Gao, 2017, Temporal and spatial evolution of the standardized precipitation evapotranspiration index (SPEI) in the Loess Plateau under climate change from 2001 to 2050, Sci Total Environ, 595, 191, 10.1016/j.scitotenv.2017.03.226 Gardner, 1998, Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627, 10.1016/S1352-2310(97)00447-0 Granger, 1969, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 37, 424, 10.2307/1912791 Granger, 1980, Testing for causality. A personal viewpoint, J. Econ. Dyn. Control., 2, 329, 10.1016/0165-1889(80)90069-X Gupta, 2018, Investigation of multi-model spatiotemporal mesoscale drought projections over India under climate change scenario, J. Hydrol., 567, 489, 10.1016/j.jhydrol.2018.10.012 Gupta, 2020, Impact of ENSO, global warming, and land surface elevation on extreme precipitation in India, J. Hydrol. Eng., 25, 10.1061/(ASCE)HE.1943-5584.0001872 Gupta, 2020, Multivariate modeling of projected drought frequency and hazard over India, J. Hydrol. Eng., 25, 10.1061/(ASCE)HE.1943-5584.0001893 Higgins, 2000, Extreme precipitation events in the Western United States related to tropical forcing, J. Clim., 13, 793, 10.1175/1520-0442(2000)013<0793:EPEITW>2.0.CO;2 Hmamouche, 2019, Predictors extraction in time series using authorities-hubs ranking, 1070 Hua, 2016, Possible causes of the Central Equatorial African long-term drought, Environ. Res. Lett., 11, 124002, 10.1088/1748-9326/11/12/124002 Hu, 2019, Impacts of idealized land cover changes on climate extremes in Europe, Ecol. Indic., 104, 626, 10.1016/j.ecolind.2019.05.037 Huang, 2018, Analysis of future drought characteristics in China using the regional climate model CCLM, Clim Dyn, 50, 507, 10.1007/s00382-017-3623-z Jiang, 2015, Observational evidence for impacts of vegetation change on local surface climate over northern China using the Granger causality test, J. Geophys. Res. Biogeosci., 120, 1, 10.1002/2014JG002741 Kaufmann, 2007, Climate response to rapid urban growth: evidence of a human-induced precipitation deficit, J. Clim., 20, 2299, 10.1175/JCLI4109.1 Kumar, 1999, On the weakening relationship between the Indian Monsoon and ENSO, Science (New York, N.Y.), 284, 2156, 10.1126/science.284.5423.2156 Kumar, 2006, Unraveling the mystery of Indian monsoon failure during El Niño, Science, 314, 115, 10.1126/science.1131152 Kumar, 2007, On the recent strengthening of the relationship between ENSO and northeast monsoon rainfall over South Asia, Clim. Dyn., 10.1007/s00382-006-0210-0 Kumar, 2013, On the observed variability of monsoon droughts over India, Weather and Climate Extremes, 1, 42, 10.1016/j.wace.2013.07.006 Kuswanto, 2019, Evaluation of performance of drought prediction in Indonesia based on TRMM and MERRA-2 using machine learning methods, MethodsX, 6, 1238, 10.1016/j.mex.2019.05.029 Kwan, 1995, The causal relationships between equity indices on world exchanges, Appl. Econ., 27, 33, 10.1080/00036849500000005 Lanckriet, 2015, Droughts related to quasi-global oscillations: a diagnostic teleconnection analysis in North Ethiopia, Int. J. Climatol., 35, 1534, 10.1002/joc.4074 Liao, 2009, Kernel granger causality mapping effective connectivity on fMRI data, IEEE Trans. Med. Imaging, 28, 1825, 10.1109/TMI.2009.2025126 Lima, 2017, Droughts in Amazonia: spatiotemporal variability, teleconnections, and seasonal predictions, Water Resour. Res., 53, 10824, 10.1002/2016WR020086 Ma, 2018, 2015–16 floods and droughts in China, and its response to the strong El Niño, Sci. Total Environ., 627, 1473, 10.1016/j.scitotenv.2018.01.280 Mallya, 2016, Trends and variability of droughts over the Indian monsoon region, Weather Clim. Extreme., 12, 43, 10.1016/j.wace.2016.01.002 Marwala, 2015 McGraw, 2018, Memory matters: a case for granger causality in climate variability studies, J. Clim., 31, 3289, 10.1175/JCLI-D-17-0334.1 Mokhov, 2006, El Niño-Southern Oscillation drives North Atlantic Oscillation as revealed with nonlinear techniques from climatic indices, Geophys. Res. Lett., 33, 10.1029/2005GL024557 Mokhov, 2011, Alternating mutual influence of El-Nio/Southern Oscillation and Indian monsoon, Geophys. Res. Lett., 38 Mosedale, 2006, Granger causality of coupled climate processes: Ocean feedback on the North Atlantic Oscillation, J. Clim., 19, 1182, 10.1175/JCLI3653.1 Nanda, 2018, Understanding plot‐scale hydrology of Lesser Himalayan watershed—A field study and HYDRUS‐2D modelling approach, Hydrol. Process., 32, 1254, 10.1002/hyp.11499 Nanda, 2019, How spatiotemporal variation of soil moisture can explain hydrological connectivity of infiltration-excess dominated hillslope: Observations from lesser Himalayan landscape, J. Hydrol., 579, 124146, 10.1016/j.jhydrol.2019.124146 Ndehedehe, 2018, Changes in hydrometeorological conditions over tropical West Africa (1980–2015) and links to global climate, Glob. Planet. Chang., 162, 321, 10.1016/j.gloplacha.2018.01.020 Ndehedehe, 2019, Modelling the impacts of global multi-scale climatic drivers on hydro-climatic extremes (1901–2014) over the Congo basin, Sci. Total Environ., 651, 1569, 10.1016/j.scitotenv.2018.09.203 Ndehedehe, 2020, Evolutionary drought patterns over the Sahel and their teleconnections with low frequency climate oscillations, Atmos. Res., 233, 10.1016/j.atmosres.2019.104700 Newman, 2011, Natural variation in ENSO flavors, Geophys. Res. Lett., 38, 1, 10.1029/2011GL047658 Oguntunde, 2017, Impacts of climate change on hydro-meteorological drought over the Volta Basin, West Africa, Glob Planet Change, 155, 121, 10.1016/j.gloplacha.2017.07.003 Pai, 2011, District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI), Nat. Hazards, 59, 1797, 10.1007/s11069-011-9867-8 Pai, 2014, Development of a new high spatial resolution (0.25× 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, Mausam, 65, 1), 1, 10.54302/mausam.v65i1.851 Paul, 2004, Causality between energy consumption and economic growth in India: a note on conflicting results, Energy Econ., 26, 977, 10.1016/j.eneco.2004.07.002 Rajagopalan, 2000, Spatiotemporal variability of ENSO and SST teleconnections to summer drought over the United States during the twentieth century, J. Clim., 13, 4244, 10.1175/1520-0442(2000)013<4244:SVOEAS>2.0.CO;2 Räsänen, 2016, On the spatial and temporal variability of ENSO precipitation and drought teleconnection in mainland Southeast Asia, Clim. Past, 12, 1889, 10.5194/cp-12-1889-2016 Schwing, 2002, The Northern Oscillation Index (NOI): a new climate index for the Northeast Pacific, Prog. Oceanogr., 53, 115, 10.1016/S0079-6611(02)00027-7 Singh, 2019, Spatiotemporal assessment of drought hazard, vulnerability and risk in the Krishna River basin, India, Nat. Hazards, 99, 611, 10.1007/s11069-019-03762-6 Smirnov, 2016, The relative importance of climate change and population growth for exposure to future extreme droughts, Clim Change, 138, 41, 10.1007/s10584-016-1716-z Spinoni, 2018, Will drought events become more frequent and severe in Europe?, Int J Climatol, 38, 1718, 10.1002/joc.5291 Srivastava, 2009, Development of a high resolution daily gridded temperature data set (1969-2005) for the Indian region, Atmos. Sci. Lett., 10, 249 Stagge, 2014, Standardized precipitation-evapotranspiration index (SPEI): Sensitivity to potential evapotranspiration model and parameters, vol. 363, 367 Stuecker, 2013, A combination mode of the annual cycle and the El Niño/Southern Oscillation, Nat. Geosci., 6, 540, 10.1038/ngeo1826 Sun, 2016, Century-scale causal relationships between global dry/wet conditions and the state of the Pacific and Atlantic Oceans, Geophys. Res. Lett., 43, 6528, 10.1002/2016GL069628 Tian, 2018, Agricultural drought prediction using climate indices based on support vector regression in Xiangjiang River basin, Sci. Total Environ., 622, 710, 10.1016/j.scitotenv.2017.12.025 Torres-Valcárcel, 2018, Teleconnections between ENSO and rainfall and drought in Puerto Rico, Int. J. Climatol., 38, e1190, 10.1002/joc.5444 Trenberth, 1997, The Definition of El Niño, Bull. Am. Meteorol. Soc., 78, 2771, 10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2 Trenberth, 1997, The definition of El Nino, Bull. Am. Meteorol. Soc., 78, 2771, 10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2 Wang, 2015, Assessing the impact of ENSO on drought in the U.S. Southwest with NCEP climate model simulations, J. Hydrol., 526, 30, 10.1016/j.jhydrol.2014.12.012 Wu, 2016, Meteorological drought in the Beijiang River basin, South China: current observations and future projections, Stochastic environmental research and risk assessment, 30, 1821, 10.1007/s00477-015-1157-7 Yang, 2017, Sensitivity of potential evapotranspiration estimation to the Thornthwaite and Penman–Monteith methods in the study of global drylands, Adv. Atmos. Sci., 34, 1381, 10.1007/s00376-017-6313-1 Yang, 2018, Unveiling neural coupling within the sensorimotor system: directionality and nonlinearity, Eur. J. Neurosci., 48, 2407, 10.1111/ejn.13692 Yao, 2018, Multi-scale assessments of droughts: a case study in Xinjiang, China, Sci. Total Environ., 630, 444, 10.1016/j.scitotenv.2018.02.200 Yulaeva, 1994, The signature of ENSO in global temperature and precipitation fields derived from the microwave sounding unit, J. Clim., 7, 1719, 10.1175/1520-0442(1994)007<1719:TSOEIG>2.0.CO;2 Yun, 2018, Decadal Monsoon-ENSO Relationships Reexamined, Geophys. Res. Lett., 45, 2014, 10.1002/2017GL076912 Zhang, 2018, Standardized precipitation evapotranspiration index is highly correlated with total water storage over China under future climate scenarios, Atmos Environ, 194, 123, 10.1016/j.atmosenv.2018.09.028