Abstract
Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal’s Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.
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Accadia C, Mariani S, Casaioli M, Lavagnini A, Speranza A (2003) Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Weather Forecast 18:918–932. https://doi.org/10.1175/1520-0434(2003)018<0918:sopfss>2.0.co;2
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P-P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167. https://doi.org/10.1175/1525-7541(2003)004<1147:tvgpcp>2.0.co;2
Asseng S, Ewert F, Martre P, Rötter RP, Lobell DB, Cammarano D, Kimball BA, Ottman MJ, Wall GW, White JW, Reynolds MP, Alderman PD, Prasad PVV, Aggarwal PK, Anothai J, Basso B, Biernath C, Challinor AJ, De Sanctis G, Doltra J, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kersebaum KC, Koehler AK, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen JE, Palosuo T, Priesack E, Eyshi Rezaei E, Ruane AC, Semenov MA, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn PJ, Waha K, Wang E, Wallach D, Wolf J, Zhao Z, Zhu Y (2015) Rising temperatures reduce global wheat production. Nat Clim Chang 5:143–147. https://doi.org/10.1038/nclimate2470
Bai J, Chen X, Dobermann A, Yang H, Cassman KG, Zhang F (2010) Evaluation of NASA satellite- and model-derived weather data for simulation of maize yield potential in China. Agron J 102:9–16. https://doi.org/10.2134/agronj2009.0085
Baigorria GA, Jones JW, Shin D, Mishra A, Brien JJ (2007) Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Clim Res 34:211–222
Besten N, Pande S, Savenije HHG (2016) A socio-hydrological comparative assessment explaining regional variances in suicide rate amongst farmers in Maharashtra, India. Proc IAHS 373:115–118
Charney JG, Shukla J (1981) Predictability of monsoons. In: Lighthill SJ (ed) monsoon dynamics
Chaudhari HS, Pokhrel S, Mohanty S, Saha SK (2013) Seasonal prediction of Indian summer monsoon in NCEP coupled and uncoupled model. Theor Appl Climatol 114:459–477. https://doi.org/10.1007/s00704-013-0854-8
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. https://doi.org/10.1002/qj.828
Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. Journal of Geophysical Research: Atmospheres 108:n/a-n/a https://doi.org/10.1029/2002jd003296
Gadgil S, Rajeevan M, Nanjundiah RS (2005) Monsoon prediction—why yet another failure? Curr Sci 88:1389–1400
Galvin JFP (2016) An introduction to meteorology and climate of the tropics. Wiley Blackwell,
Geng S, Penning de Vries FWT, Supit I (1986) A simple method for generating daily rainfall data. Agric For Meteorol 36:363–376. https://doi.org/10.1016/0168-1923(86)90014-6
Goswami BN (1998) Interannual variations of Indian summer monsoon in a GCM: external conditions versus internal feedbacks. J Climate 11:501–522
Griffies SM, Harrison MJ, Pacanowski RC, Rosati A (2004) Technical guide to MOM4. NOAA/Geophysical Fluid Dynamics Laboratory,
Gutierrez AP, Ponti L, Herren HR, Baumgärtner J, Kenmore PE (2015) Deconstructing Indian cotton: weather, yields, and suicides. Environ Sci Eur 27:12. https://doi.org/10.1186/s12302-015-0043-8
Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe D (2011) Climate impacts on agriculture: implications for crop production. Agron J 103:351–370. https://doi.org/10.2134/agronj2010.0303
Hoogenboom G (2000) Contribution of agrometeorology to the simulation of crop production and its applications. Agric For Meteorol 103:137–157. https://doi.org/10.1016/S0168-1923(00)00108-8
IRRI (2009) World Rice statistics 2009 derived from FAO 2004–2006 database—three years’ average. International Rice Research Institute, Los Banos, Laguna, Phillipines
IRRI (2010) Household survey data for Nepal collected under IFAD upland rice and STRASA (stress tolerant rice for poor farmers in Africa and South Asia) projects. Social Sciences Division, International Rice Research Institute, Los Banos, Phillipines
Jimoh OD, Webster P (1996) The optimum order of a Markov chain model for daily rainfall in Nigeria. J Hydrol 185:45–69. https://doi.org/10.1016/S0022-1694(96)03015-6
Johnson GL, Hanson CL, Hardegree SP, Ballard EB (1996) Stochastic weather simulation: overview and analysis of two commonly used models. J Appl Meteorol 35:1878–1896. https://doi.org/10.1175/1520-0450(1996)035<1878:swsoaa>2.0.co;2
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265
Kim H-M, Webster PJ, Curry JA (2012) Seasonal prediction skill of ECMWF system 4 and NCEP CFSv2 retrospective forecast for the northern hemisphere winter. Clim Dyn 39:2957–2973. https://doi.org/10.1007/s00382-012-1364-6
Krishnamurthy V, Shukla J (2012) Predictability of the Indian monsoon in coupled generral circulation models. In: Tyagi (ed)
Kug J-S, Kang I-S, Choi D-H (2008) Seasonal climate predictability with tier-one and tier-two prediction systems. Clim Dyn 31:403–416. https://doi.org/10.1007/s00382-007-0264-7
Marahatta S, Dongol BS, Gurung GB (2009) Temporal and spatial variability of climate change over Nepal (1976-2005)
Mikhail AS, Roger JB, Elaine MB, Clarence WR (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107
Mishra S (2006) Farmers’ suicides in Maharashtra. Econ Polit Wkly 41:1538–1545
MOAC (2008) Statistical informaton on Nepalese agriculture, 2008/09, Government of Nepal, Ministry of Agricultural Development, Agribusiness Promotion and Statistics Division, Singh Durbar, Kathmandu,Nepal
MOAD (2012) Statistical informaton on Nepalese agriculture, 2011/12, Government of Nepal, Ministry of Agricultural Development, Agribusiness Promotion and Statistics Division, Singh Durbar, Kathmandu,Nepal
Mohanty UC, Routray A, Osuri KK, Kiran Prasad S (2012) A study on simulation of heavy rainfall events over Indian region with ARW-3DVAR modeling system. Pure Appl Geophys 169:381–399. https://doi.org/10.1007/s00024-011-0376-1
Monteiro LA, Sentelhas PC, Pedra GU (2017) Assessment of NASA/POWER satellite-based weather system for Brazilian conditions and its impact on sugarcane yield simulation. Int J Climatol. https://doi.org/10.1002/joc.5282
Monteith JL (1977) Climate and the efficiency of crop production in Britain. Philos Trans Royal Soc London B Biol Sci 281:277–294. https://doi.org/10.1098/rstb.1977.0140
Moorthi S, Pan H, Caplan P (2001) Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NCEP,
MOPE (2004) Initial National Communication to the conference of parties of the United Nations framework convention on climate change. Kathmandu
Pokhrel S, Saha SK, Dhakate A, Rahman H, Chaudhari HS, Salunke K, Hazra A, Sujith K, Sikka DR (2016) Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error. Clim Dyn 46:2305–2326. https://doi.org/10.1007/s00382-015-2703-1
Rajeevan M, Pai DS, Anil Kumar R, Lal B (2007) New statistical models for long-range forecasting of southwest monsoon rainfall over India. Clim Dyn 28:813–828. https://doi.org/10.1007/s00382-006-0197-6
Ray DK, Gerber JS, MacDonald GK, West PC (2015) Climate variation explains a third of global crop yield variability. Nat Commun 6:5989. https://doi.org/10.1038/ncomms6989
Remesan R, Holman IP (2015) Effect of baseline meteorological data selection on hydrological modelling of climate change scenarios. J Hydrol 528:631–642. https://doi.org/10.1016/j.jhydrol.2015.06.026
Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17:182–190. https://doi.org/10.1029/WR017i001p00182
Ritchie JT, Singh U, Godwin DC, Bowen WT (1998) Cereal growth, development and yield. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Springer Netherlands, Dordrecht, pp 79–98. https://doi.org/10.1007/978-94-017-3624-4_5
Roncoli C, Jost C, Kirshen P, Sanon M, Ingram KT, Woodin M, Somé L, Ouattara F, Sanfo BJ, Sia C, Yaka P, Hoogenboom G (2008) From accessing to assessing forecasts: an end-to-end study of participatory climate forecast dissemination in Burkina Faso (West Africa). Clim Chang 92:433–460. https://doi.org/10.1007/s10584-008-9445-6
Saha S, Moorthi S, Pan H-L, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu H, Stokes D, Grumbine R, Gayno G, Wang J, Hou Y-T, Chuang H-Y, Juang H-MH, Sela J, Iredell M, Treadon R, Kleist D, Van Delst P, Keyser D, Derber J, Ek M, Meng J, Wei H, Yang R, Lord S, Van Den Dool H, Kumar A, Wang W, Long C, Chelliah M, Xue Y, Huang B, Schemm J-K, Ebisuzaki W, Lin R, Xie P, Chen M, Zhou S, Higgins W, Zou C-Z, Liu Q, Chen Y, Han Y, Cucurull L, Reynolds RW, Rutledge G, Goldberg M (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057. https://doi.org/10.1175/2010bams3001.1
Saha SK, Pokhrel S, Chaudhari HS, Dhakate A, Shewale S, Sabeerali CT, Salunke K, Hazra A, Mahapatra S, Rao AS (2014) Improved simulation of Indian summer monsoon in latest NCEP climate forecast system free run. Int J Climatol 34:1628–1641. https://doi.org/10.1002/joc.3791
Samir P, Dhakate A, Chaudhari HS, Saha SK (2013) Status of NCEP CFS vis-a-vis IPCC AR4 models for the simulation of Indian summer monsoon. Theor Appl Climatol 11:65–78
Schmidt GM, Smajstrla AG, Zazueta FS (1996) Parametric uncertainty in stochastic precipitation models: wet day amounts. Trans ASAE 39:2093–2103
Shah R, Mishra V (2014) Evaluation of the reanalysis products for the monsoon season droughts in India. J Hydrometeorol 15:1575–1591. https://doi.org/10.1175/JHM-D-13-0103.1
Stockdale TN, Anderson DLT, Balmaseda MA, Doblas-Reyes F, Ferranti L, Mogensen K, Palmer TN, Molteni F, Vitart F (2011) ECMWF seasonal forecast system 3 and its prediction of sea surface temperature. Clim Dyn 37:455–471. https://doi.org/10.1007/s00382-010-0947-3
Thompson LM (1969) Weather and technology in the production of corn in the US corn belt. Agron J 61:453–456
Thorne PW, Vose RS (2010) Reanalyses suitable for characterizing long-term trends. Bull Am Meteorol Soc 91:353–361. https://doi.org/10.1175/2009BAMS2858.1
van Wart J, Grassini P, Cassman KG (2013) Impact of derived global weather data on simulated crop yields. Glob Chang Biol 19:3822–3834. https://doi.org/10.1111/gcb.12302
Walker GT (1924) Correlation in seasonal variations of weather, IX: a further study of world weather. Mem India Meteor Dep 24:275–332
Wang B, Ding Q, Fu X, Kang I-S, Jin K, Shukla J, Doblas-Reyes F (2005) Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys Res Lett 32:L15711. https://doi.org/10.1029/2005gl022734
Webster PJ, Magaña VO, Palmer TN, Shukla J, Tomas RA, Yanai M, Yasunari T (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Res Oceans 103:14451–14510. https://doi.org/10.1029/97jc02719
White JW, Hoogenboom G, Wilkens PW, Stackhouse PW, Hoel JM (2011) Evaluation of satellite-based, modeled-derived daily solar radiation data for the continental United States. Agron J 103:1242–1251. https://doi.org/10.2134/agronj2011.0038
Winton M (2000) A reformulated three-layer sea ice model. J Atmos Ocean Technol 17:525–531. https://doi.org/10.1175/1520-0426(2000)017<0525:artlsi>2.0.co;2
Wu X, Simmonds I, Budd WF (1997) Modeling of Antarctic Sea ice in a general circulation model. J Clim 10:593–609. https://doi.org/10.1175/1520-0442(1997)010<0593:moasii>2.0.co;2
Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc 93:1401–1415. https://doi.org/10.1175/bams-d-11-00122.1
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Jha, P.K., Athanasiadis, P., Gualdi, S. et al. Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal’s Terai with the NCEP CFSv2. Theor Appl Climatol 135, 1143–1156 (2019). https://doi.org/10.1007/s00704-018-2433-5
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DOI: https://doi.org/10.1007/s00704-018-2433-5