Comparing and Evaluating Ensemble Generation Techniques from Multi-Model Climate Data for Wind Speed Projection in Rio Grande do Norte for the Present and Future
Abstract - 84
PDF

Keywords

RCP8.5
CORDEX
Wind energy
Arithmetic mean
Convex combination
Regional climate models

How to Cite

1.
Gurgel A de R, Alves RT. Comparing and Evaluating Ensemble Generation Techniques from Multi-Model Climate Data for Wind Speed Projection in Rio Grande do Norte for the Present and Future. Glob. J. Earth Sci. Eng. [Internet]. 2025 Jun. 25 [cited 2025 Jul. 30];12:14-29. Available from: https://www.avantipublishers.com/index.php/gjese/article/view/1593

Abstract

This study presents a comparative evaluation of ensemble generation techniques for projecting wind speed in the state of Rio Grande do Norte, Brazil, utilizing regional climate models from the CORDEX initiative. Two approaches—Arithmetic Mean (AM) and Convex Combination (CC)—were assessed for the historical period (1994–2023) and for future projections (2031–2060) under the high-emission RCP 8.5 scenario. The findings demonstrate that the AM method consistently outperforms CC, exhibiting higher correlation coefficients and lower root mean square error (RMSE) values across all subregions analyzed. Specifically, the AM ensemble achieved correlation coefficients of 0.88, 0.86, and 0.80 in the northern, central, and eastern regions, respectively, exceeding those of the CC method (0.85, 0.84, and 0.78). Relative to present-day conditions, projected future wind speeds increase by approximately 12.2% in the northern region, 23.5% in the eastern region, and 19.6% in the central region. A notable seasonal shift was also observed, with peak wind speeds occurring later in the year across all areas. These projected increases, when considered in light of the cubic relationship between wind speed and energy production, suggest that wind power potential may rise by over 40% in certain regions. It is also important to acknowledge that such results are subject to uncertainties inherent in climate modeling, including the structural differences among regional and global models and their associated physical parameterizations. Nonetheless, the projected enhancement in wind speed holds significant implications for strategic renewable energy planning in Rio Grande do Norte and reinforces the utility of multi-model ensemble techniques in climate-based energy assessments.

https://doi.org/10.15377/2409-5710.2025.12.2
PDF

References

Hamed TA, Alshare A. Environmental impact of solar and wind energy- a review. J Sustain Dev Energy Water Environ Syst. 2022; 10(2): 1090387. https://doi.org/10.13044/j.sdewes.d9.0387

Associação Brasileira de Energia Eólica – ABEEólica. ABEEólica. Available at: https://abeeolica.org.br/ (accessed on September 3, 2024).

Marengo JA, Torres RR, Alves LM. Drought in Northeast Brazil—Past, present, and future. Theor Appl Climatol. 2017; 129(3-4): 1189-200. https://doi.org/10.1007/s00704-016-1840-8

Vásquez P IL, Barbosa HA, Sampaio G, Sánchez P CA, Utida G, Quispe DP, et al. Multidecadal variability of the ITCZ from the Last Millennium Extreme Precipitation Changes in Northeastern Brazil. EGUsphere. 2022; 2022: 1-26. https://doi.org/10.5194/egusphere-2022-785

Reboita MS, Ambrizzi T, Silva BA, Pinheiro RF and da Rocha RP. The South Atlantic Subtropical Anticyclone: Present and future climate. Front Earth Sci. 2019; 7: 8. https://doi.org/10.3389/feart.2019.00008

Ruiz SAG, Barriga JEC, Martínez JA. Wind power assessment in the Caribbean region of Colombia, using ten-minute wind observations and ERA5 data. Renew Energy. 2021; 172: 158-76. https://doi.org/10.1016/j.renene.2021.03.033

Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146(730): 1999-2049. https://doi.org/10.1002/qj.3803

Giorgi F, Jones C, Asrar G. Addressing climate information needs at the regional level: the CORDEX framework. WMO Bull. 2009;58(3):175. Available from: https://www.adaptation-changement-climatique.gouv.fr/sites/cracc/files/inline-files/CORDEX1.pdf (accessed on Jan 13, 2025).

Reboita MS, Amaro TR, de Souza MR. Winds: Intensity and power density simulated by RegCM4 over South America in present and future climate. Clim Dyn. 2018; 51: 187-205. https://doi.org/10.1007/s00382-017-3913-5

Sawadogo W, Reboita MS, Faye A, Rocha RP, Odoulami RC, Olusegun CF, et al. Current and future potential of solar and wind energy over Africa using the RegCM4 CORDEX-CORE ensemble. Clim Dyn. 2021; 57: 1647-72. https://doi.org/10.1007/s00382-020-05377-1

Gross M, Magar V. Offshore wind energy climate projection using UPSCALE climate data under the RCP8.5 emission scenario. PLoS One. 2016; 11(10): e0165423. https://doi.org/10.1371/journal.pone.0165423

Davy R, Gnatiuk N, Pettersson L, Bobylev L, Climate change impacts on wind energy potential in the European domain with a focus on the Black Sea, Renew Sustain Energy Rev. 2018; 81(2): 1652-9. https://doi.org/10.1016/j.rser.2017.05.253

Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim Change. 2011; 109: 33-57. https://doi.org/10.1007/s10584-011-0149-y

Wu R, Niu X, Jing X, Li P, Mao Y, Chen X, et al. Future projection and uncertainty analysis of wind and solar energy in China based on an ensemble of CORDEX-EA-II regional climate simulations. J Geophys Res Atmos. 2024; 129(6): e2023JD040271. https://doi.org/10.1029/2023JD040271

Wu Y, Miao C, Fan X, Miao C. Quantifying the uncertainty sources of future climate projections and narrowing uncertainties with bias correction techniques. J Adv Model Earth Syst. 2022; 14(11): e2022MS003392. https://doi.org/10.1029/2022EF002963

Gurgel ARC, Sales DC, Lima KC. Wind power density in areas of Northeastern Brazil from regional climate models for a recent past. PLoS One. 2024; 19(7): e0307641. https://doi.org/10.1371/journal.pone.0307641

Coutinho MDL, Lima KC, Santos e Silva CM. Improvements in precipitation simulation over South America for past and future climates via multi-model combination. Clim Dyn. 2017; 49: 343-61. https://doi.org/10.1007/s00382-016-3346-6

Long H, Geng R, Zhang C. Wind speed interval prediction based on the hybrid ensemble model with biased convex cost function. Front Energy Res. 2022; 10: 954274. https://doi.org/10.3389/fenrg.2022.954274

Barros JD, Furtado MLS, Costa AMB, Marinho GS, Silva FM. Sazonalidade do vento na cidade de Natal/RN pela distribuição de Weibull. Soc Territ. 2013; 25(2): 78-92.

Elguindi N, Giorgi F, Solmon F, Gao X, Rauscher S, Coppola E, et al. Regional climate model RegCM: Reference manual version 4.7. Trieste: Abdus Salam ICTP; 2017. Available from: https://www.researchgate.net/profile/Muhammadreza_Tabatabaei/post/

Uncertainty_in_Climate_Models/attachment/59eef385b53d2fe117b8794c/AS:552896195502080%401508832133118/download/ReferenceMan.pdf

Collins WJ, Bellouin N, Doutriaux-Boucher M, Gedney N, Halloran P, Hinton T, et al. Development and evaluation of an Earth-system model – HadGEM2. Geosci Model Dev. 2011; 4: 1051-1075. https://doi.org/10.5194/gmd-4-1051-2011

Zhang ZS, Nisancioglu K, Bentsen M, Tjiputra J, Bethke I, Yan Q, et al. Pre-industrial and mid-Pliocene simulations with NorESM-L. Geosci Model Dev. 2012; 5(2): 523-33. https://doi.org/10.5194/gmd-5-523-2012

Zanchettin D, Rubino A, Matei D, Bothe O, Jungclaus JH. Multidecadal-to-centennial SST variability in the MPI-ESM simulation ensemble for the last millennium. Clim Dyn. 2012; 40(6): 1301-18. https://doi.org/10.1007/s00382-012-1361-9

Ambrizzi T, Reboita MS, Rocha RP, Llopart M. The state of the art and fundamental aspects of regional climate modeling in South America. Ann N Y Acad Sci. 2019; 1436(1): 98-120. https://doi.org/10.1111/nyas.13932

Giorgi F, Mearns LO. Approaches to the simulation of regional climate change: A review. Rev Geophys. 1991; 29(2): 191-216. https://doi.org/10.1029/90RG02636

Takle ES, Roads J, Rockel B, Gutowski WJ, Arritt RW, Meinke I, et al. Transferability intercomparison: An opportunity for new insight on the global water cycle and energy budget. Bull Am Meteorol Soc. 2007; 88(3): 375-84. https://doi.org/10.1175/BAMS-88-3-375

Tebaldi C, Knutti R. The use of the multi-model ensemble in probabilistic climate projections. Philos Trans A Math Phys Eng Sci. 2007; 365(1857): 2053-75. https://doi.org/10.1098/rsta.2007.2076

Yazd HRGH, Salehnia N, Kolsoumi S, Hoogenboom G. Prediction of climate variables by comparing the k-nearest neighbor method and MIROC5 outputs in an arid environment. Clim Res. 2018; 76(4): 303-18. https://doi.org/10.3354/cr01545

Hagedorn R, Doblas-Reyes FJ, Palmer TN. The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept. Tellus A Dyn Meteorol Oceanogr. 2005; 57(3): 219-33. https://doi.org/10.3402/tellusa.v57i3.14657

Knutti R, Abramowitz G, Collins M, Eyring V, Gleckler PJ, et al. Good practice guidance paper on assessing and combining multi model climate projections. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Midgley PM, Eds. Meeting Report of the IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections. Bern, Switzerland: IPCC Working Group I Technical Support Unit, University of Bern; 2010. p. 1-15.

Coutinho MM. Previsão por Conjunto Utilizando Perturbações Baseadas em Componentes Principais. Master's Dissertation, National Institute for Space Research (INPE), São José dos Campos, Brazil 1999.

Paiva AP, Ferreira JR, Paiva EJ, Balestrassi PP, Costa SC. A multivariate mean square error optimization of AISI 52100 hardened steel turning. Int J Adv Manuf Technol. 2008; 43: 631-43. https://doi.org/10.1007/s00170-008-1745-5

Andrade DDF. Métodos Quantitativos – Pesquisa Operacional. Vol. III. Belo Horizonte: Poisson; 2018.

Taylor KE. Summarizing multiple aspects of model performance in a single diagram. J Geophys Res. 2001; 106(D7): 7183-92. https://doi.org/10.1029/2000JD900719

Wilks DS. Statistical Methods in the Atmospheric Sciences. 3rd ed. Oxford: Academic Press; 2011.

Devore JL. Probability and Statistics for Engineering and the Sciences. São Paulo: Thomson Pioneira; 2006. p. 706.

Reboita MS, Krusche N, Ambrizzi T, Rocha RP. Entendendo o tempo e o clima na América do Sul. Terræ Didat. 2012; 8(1): 34-50. Available from: https://www.ige.unicamp.br/terraedidatica/v8-1/pdf81/s3.pdf

Ribeiro RMR, Vitorino MI, Moura MN. Variabilidade sazonal da Zona de Convergência Intertropical e sua influência sobre o norte da América do Sul. Rev Bras Geogr Fis. 2023; 16(5): 2798-810. https://doi.org/10.26848/rbgf.v16.5.p2798-2810

Santos ATS, Silva CMS. Seasonality, interannual variability, and linear tendency of wind speeds in the Northeast Brazil from 1986 to 2011. Sci World J. 2013; 2013: Article ID 490857. https://doi.org/10.1155/2013/490857

Weigel AP, Knutti R, Liniger MA, Appenzeller C. Risks of model weighting in multimodel climate projections. J Clim. 2010; 23(15): 4175-91. https://doi.org/10.1175/2010JCLI3594.1

Brands S, Herrera S, Fernández J, Gutiérrez JM. How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa? Clim Dyn. 2013; 41(3): 803-17. https://doi.org/10.1007/s00382-013-1742-8

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2025 Augusto de R.C. Gurgel, Roberta T.S.F.D. Alves

Downloads

Download data is not yet available.