Abstract
One of the key topics in scientific research on the effect of global climate change is to study the variations in the global carbon cycle. In this respect, net primary production (NPP) is an important component of carbon storage and a key indicator for evaluating ecosystem functions. However, there are many climatic factors and variables, both at the local and global levels, that can influence NPP variations at various time scales, including daily, monthly, seasonal, and even annual. So, it is of crucial significance for stakeholders and ecosystem managers to understand these variables and their relationship with NPP variations in different ecosystems with different vegetation types, especially in arid and semi-arid regions. Thus, this research investigated the spatiotemporal linear and nonlinear relationships between NPP variations of different bioclimatic zones of Iran and the atmospheric-oceanic oscillations on a monthly and seasonal basis from 2000 to 2016 using generalized linear and additive regression models (GLMs and GAMs). For this purpose, the NPP values were derived from the MODIS satellite products to check the effect of teleconnection indices including Arctic Oscillation (AO), Antarctic Oscillation (AAO), Atlantic Multi-decadal Oscillation (AMO), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), Western Mediterranean Oscillation (WMO), and El Niño-Southern Oscillation indices (NINO) including NINO1.2, NINO3.4, NINO3, and NINO4 on it. Findings showed that almost all of the studied climatic indices were influential on the seasonal NPP variations in Iran’s bioclimatic regions with various temporal and spatial patterns. It is revealed that AO, AAO, AMO, NAO, SOI, and WMO indices influence the bioclimatic zones perpetually over the year. However, some indices, such as NINO3.4 in spring, NINO.4 and SOI in summer, and NAO and AMO during warm phases in autumn, have more significant impacts on NPP variations in a given season of the year. However, the model adequacy statistics showed that the NINO family indices and the NAO index were more influential on NPP variations, especially in the winter and spring, than the other climatic indices. In general, the results revealed that the relationship between the NINO family indices and NPP was ascending, either in the linear form or in the nonlinear form. In other words, the NPP value will increase in the bioclimatic regions of Iran during the El Niño phases.
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References
Abdi AM, Vrieling A, Yengoh GT et al (2016) The El Niño – La Niña cycle and recent trends in supply and demand of net primary productivity in African drylands. Clim Change 138:111–125. https://doi.org/10.1007/S10584-016-1730-1/FIGURES/5
Ahmadaali K, Eskandari Damaneh H, Ababaei B, Eskandari Damaneh H (2021a) Impacts of droughts on rainfall use efficiency in different climatic zones and land uses in Iran. Arab J Geosci 14. https://doi.org/10.1007/s12517-020-06389-1
Ahmadaali K, EskandariDamaneh H, Ababaei B, EskandariDamaneh H (2021b) Impacts of droughts on rainfall use efficiency in different climatic zones and land uses in Iran. Arab J Geosci 14:126. https://doi.org/10.1007/s12517-020-06389-1
Aamir E, Hassan I (2020) The impact of climate indices on precipitation variability in Baluchistan, Pakistan. Tellus a: Dynam Meteorol Oceanog 72(1):1–46. https://doi.org/10.1080/16000870.2020.1833584
Aamir E, Khan A, Abubakar Tariq M (2022) The influence of teleconnections on the precipitation in Baluchistan. Atmosphere 13(7):1001–1021. https://doi.org/10.3390/atmos13071001
Bastos A, Running SW, Gouveia C, Trigo RM (2013) The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J Geophys Res Biogeosci 118:1247–1255. https://doi.org/10.1002/JGRG.20100
Bayable G, Amare G, Alemu G (2021) Gashaw T (2021) Spatiotemporal variability and trends of rainfall and its association with Pacific Ocean Sea surface temperature in West Harerge Zone Eastern Ethiopia. Environ Syst Res 101(10):1–21. https://doi.org/10.1186/S40068-020-00216-Y
Brauner N, Shacham M (1998) Role of range and precision of the independent variable in regression of data. AIChE J 44(3):603–611
Buermann W, Anderson B, Tucker CJ et al (2003) Interannual covariability in Northern Hemisphere air temperatures and greenness associated with El Niño-Southern Oscillation and the Arctic Oscillation. J Geophys Res Atmos 108:4396. https://doi.org/10.1029/2002JD002630
Burrell AL, Evans JP, Liu Y (2017) Detecting dryland degradation using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sens Environ 197:43–57. https://doi.org/10.1016/j.rse.2017.05.018
Cai W, Borlace S, Lengaigne M et al (2014) Increasing frequency of extreme El Niño events due to greenhouse warming. Nat Clim Chang 4:111–116. https://doi.org/10.1038/nclimate2100
Camus P, Haigh ID, Wahl T, Nasr AA, Méndez FJ, Darby SE, Nicholls RJ (2022) Daily synoptic conditions associated with occurrences of compound events in estuaries along North Atlantic coastlines. Int J Climatol. https://doi.org/10.1002/joc.7556
Dehghani M, Salehi S, Mosavi A, Nabipour N, Shamshirband S, Ghamisi P (2020) Spatial analysis of seasonal precipitation over Iran: co-variation with climate indices. ISPRS Int J Geo Inf 9(2):73
Eskandari Damaneh H, Khosravi H, Habashi K, et al (2021) The impact of land use and land cover changes on soil erosion in western Iran. Nat Hazards. https://doi.org/10.1007/s11069-021-05032-w
Frederiksen CS, Zheng X, Grainger S (2021) Decadal and multidecadal variability in ERSSTv5 Global SST during 1879–2018. J Clim 34:7461–7473. https://doi.org/10.1175/JCLI-D-20-0902.1
Girishkumar MS, Ravichandran M (2012) The influences of ENSO on tropical cyclone activity in the Bay of Bengal during October–December. J Geophys Res Ocean 117. https://doi.org/10.1029/2011JC007417
Gonsamo A, Chen JM (2015) Winter teleconnections can predict the ensuing summer European crop productivity. Proc Natl Acad Sci 112:E2265–E2266. https://doi.org/10.1073/PNAS.1503450112
Gonsamo A, Chen JM, Lombardozzi D (2016a) Global vegetation productivity response to climatic oscillations during the satellite era. Glob Chang Biol 22:3414–3426. https://doi.org/10.1111/gcb.13258
Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157(2–3):89–100
Haile BT, Zeleke TT, Beketie KT et al (2021) Analysis of El Niño Southern Oscillation and its impact on rainfall distribution and productivity of selected cereal crops in Kembata Alaba Tembaro zone. Clim Serv 23:100254. https://doi.org/10.1016/j.cliser.2021.100254
Hamouda ME, Pasquero C, Tziperman E (2021) Decoupling of the Arctic Oscillation and North Atlantic Oscillation in a warmer climate. Nat Clim Chang 11(2):137–142. https://doi.org/10.1038/s41558-020-00966-8
Hastie T, Tibshirani R (1987) Generalized additive models: some applications. J Am Stat Assoc 82(398):371–386
Klavans JM, Clement AC, Cane MA, Murphy LN (2022) The evolving role of external forcing in North Atlantic SST variability over the last millennium. J Clim 35(9):2741–2754. https://doi.org/10.1175/JCLI-D-21-0338.1
Klavans JM, Cane MA, Clement AC et al. NAO predictability from external forcing in the late 20th century. npj Clim Atmos Sci 4, 22 (2021). https://doi.org/10.1038/s41612-021-00177-8
Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (2003) An overview of the North Atlantic Oscillation. In: Geophysical Monograph-American Geophysical Union. AGU AMERICAN GEOPHYSICAL UNION, pp 1–35
Kendall MG (1948) Rank correlation methods. Charles Griffin, London
Ji S, Ren S, Li Y et al (2022) The response of net primary productivity to climate change and its impact on hydrology in a water-limited agricultural basin. Environ Sci Pollut Res 29(7):10277–10290. https://doi.org/10.1007/s11356-021-16458-x
Khatibi R, Saberi M (2020) Bio-climatic classification of Iran by multivariate statistical methods. SN Appl Sci 2(10):1–30
Larsen K (2015) GAM: the predictive modeling silver bullet. Multithreaded. Stitch Fix, 30, 1-27
Lawman AE, Di Nezio PN, Partin JW, et al (2022) Unraveling forced responses of extreme El Niño variability over the Holocene. Sci. Adv, 8(9), 4313. https://doi.org/10.1126/sciadv.abm4313
Le T, Bae DH (2022) Causal impacts of El Niño–Southern Oscillation on global soil moisture over the period 2015–2100. Earth's Future, 10(3), e2021EF002522. https://www.science.org/doi/full/10.1126/sciadv.abm43133
Lemus-Canovas M (2022) Changes in compound monthly precipitation and temperature extremes and their relationship with teleconnection patterns in the Mediterranean. J Hydrol 608:127580. https://doi.org/10.1016/j.jhydrol.2022.127580
Lifland J (2003) The North Atlantic Oscillation: climatic significance and environmental impact. Eos, Trans Am Geophys Union 84:73–73. https://doi.org/10.1029/2003EO080005
Machado-Silva F, Peres LF, Gouveia CM, et al (2021) Drought resilience debt drives NPP decline in the Amazon forest. Global Biogeochem Cycles 35:e2021GB007004. https://doi.org/10.1029/2021GB007004
Mann HB (1945) Nonparametric tests against trend. Econometrica: J Econ Soc, pp. 245–259
Martínez-jauregui M, San Miguel-ayanz A, Mysterud A et al (2009) Are local weather, NDVI and NAO consistent determinants of red deer weight across three contrasting European countries? Glob Chang Biol 15:1727–1738. https://doi.org/10.1111/J.1365-2486.2008.01778.X
Meng M, Gong D, Zhu T, et al (2022) Significant association between winter North Atlantic SST and spring NDVI anomaly over Eurasia. J Geophys Res Atmos 127(9), e2021JD036315. https://doi.org/10.1029/2021JD036315
Morlini I (2006) On multicollinearity and concurvity in some nonlinear multivariate models. Stat Methods Appl 15(1):3–26
Nelder JA, Wedderburn RW (1972) Generalized linear models. J Royal Stat Soc Ser A (general) 135(3):370–384
Ngo THD, La Puente, CA (2012) The steps to follow in a multiple regression analysis. In SAS Global forum (Vol. 2012, pp. 1–12)
Nimac I, Herceg‐Bulić I, Žuvela‐Aloise M, et al (2022) Impact of North Atlantic Oscillation and drought conditions on summer urban heat load‐a case study for Zagreb. Int J Climatol. https://doi.org/10.1002/joc.7507
Potter C, Klooster S, Steinbach M et al (2004) Understanding global teleconnections of climate to regional model estimates of Amazon ecosystem carbon fluxes. Glob Chang Biol 10:693–703. https://doi.org/10.1111/j.1529-8817.2003.00752.x
Roghani R, Soltani S, Bashari H (2016) Influence of southern oscillation on autumn rainfall in Iran (1951–2011). Theoret Appl Climatol 124(1–2):411–423
Royston JP (1995) Shapiro-Wilk normality test and P-value. Appl Stat 44(4):547–551
Savari M, EskandariDamaneh H, EskandariDamaneh H (2022) Drought vulnerability assessment: solution for risk alleviation and drought management among Iranian farmers. Int J Disaster Risk Reduct 67:102654. https://doi.org/10.1016/J.IJDRR.2021.102654
Shi M, Huang Y, Fu Z (2022) Dynamical systems persistence parameter of sea surface temperature and its associations with regional averaged index over the tropical Pacific. Int J Climatol 1– 13. https://doi.org/10.1002/joc.7664
Smith DM, Scaife AA, Eade R et al (2020) (2020) North Atlantic climate far more predictable than models imply. Nat 5837818(583):796–800. https://doi.org/10.1038/s41586-020-2525-0
Taboada FG, Barton AD, Stock CA et al (2019) Seasonal to interannual predictability of oceanic net primary production inferred from satellite observations. Prog Oceanogr 170:28–39. https://doi.org/10.1016/J.POCEAN.2018.10.010
Trenberth KE, Hoar TJ (1997) El Niño and climate change. Geophys Res Lett 24:3057–3060. https://doi.org/10.1029/97GL03092
Veiga SF, Yuan H (2022) The response of the East Asian summer rainfall to more extreme El Niño events in future climate scenarios. Atmos Res 268:105983. https://doi.org/10.1016/j.atmosres.2021.105983
Vicente-Serrano SM, Delbart N, Le Toan T, Grippa M (2006) El Niño-Southern Oscillation influences on the interannual variability of leaf appearance dates in central Siberia. Geophys Res Lett 33:3707. https://doi.org/10.1029/2005GL025000
Wang L, Wei Y, Niu Z (2008) Spatial and temporal variations of vegetation in Qinghai Province based on satellite data. J Geogr Sci 2008 181 18:73–84. https://doi.org/10.1007/S11442-008-0073-X
West H, White P, Quinn N, Horswell M (2022) The spatio-temporal influence of atmospheric circulations on monthly precipitation in Great Britain. Atm 13(3):429. https://doi.org/10.3390/atmos13030429
Wood SN (2006) Generalized additive models: an introduction with R. Chapman and hall/CRC
Yang R, Xing B (2022) Teleconnections of large-scale climate patterns to regional drought in mid-latitudes: a case study in Xinjiang. China Atmosphere 13(2):230. https://doi.org/10.3390/atmos13020230
Yang Y, Hu X, Liao G, Cao Q, Chen S, Gao H, Wei X (2022) Improved ENSO and PDO prediction skill resulting from finer parameterization schemes in a CGCM. Remote Sens 14(14):3363. https://doi.org/10.3390/rs14143363
Zhang K, Kimball JS, McDonald KC et al (2007) Impacts of large-scale oscillations on pan-Arctic terrestrial net primary production. Geophys Res Lett 34:21403. https://doi.org/10.1029/2007GL031605
Zhou P, Yan H, Han T et al (2022) Mid to Late Holocene ENSO variability reconstructed by high-resolution Tridacna Sr/Ca records from the northern part of the South China Sea. Palaeogeogr Palaeoclimatol Palaeoecol 601:111117. https://doi.org/10.1016/j.palaeo.2022.111117
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Shahri, S.M.A., Soltani, S., Esfahani, M.T. et al. Effects of teleconnection indices on net primary production (NPP) in bioclimatic zones of Iran. Arab J Geosci 16, 57 (2023). https://doi.org/10.1007/s12517-022-11132-z
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DOI: https://doi.org/10.1007/s12517-022-11132-z