Statistical downscaling and wave height bias correction for Cuban coasts
Article Sidebar
Published:
Mar 19, 2025
Keywords:
BIAS correction, climate change, CMIP6, marine climate, statistical downscaling
Main Article Content
Abstract
Projections of significant wave height (Hsig) for the periods 2041-2070 (mid-term) and 2071-2100 (long-term) with respect to 1981-2010 are presented using statistical downscaling for the Cuban archipelago. Data from the ERA5 reanalysis and meteorological buoys 42003 and 42056 were used, as well as the output of five global climate models (GCM) for the climate scenarios SSP2-4.5 and SSP5-8.5. The validation methods used were Pearson's correlation coefficient (R), mean error (BIAS), root mean square error (RSME), Taylor Skill Score (TSS) and Skill Score (SS). At the same time, BIAS correction (BC) by delta method and delta quantile delta mapping (DQM) as well as bilinear interpolation were applied. The results show that the regression model used to estimate Hsig as a function of wind speed, for the mean of the five GCMs (MMM), after application of BC, showed R=0.90, RSME=0.09 m, TSS=0.81 and SS=0.64 for monthly means, while for daily data, related to the 50th and 90th percentiles, RSME ranged between 0.2-0.6 m. The annual mean Hsig is expected to increase in the Cuban coasts in all periods and climatic scenarios analyzed with respect to 1981-2010, except for SSP5-8.5 in the long term; while the extreme indicators of climate change used indicate that the Cuban marine climate in terms of Hsig would transition to be more extreme, for each of the periods and climatic scenarios analyzed.
Downloads
Download data is not yet available.
Article Details
How to Cite
Hidalgo MayoA., & Mitrani Arenal I. (2025). Statistical downscaling and wave height bias correction for Cuban coasts. Revista Cubana De Meteorología, 31(2), https://cu-id.com/2377/v31n2e03. Retrieved from http://rcm.insmet.cu/index.php/rcm/article/view/951
Section
Original Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Those authors who have publications with this journal accept the following terms of the License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0):
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
The journal is not responsible for the opinions and concepts expressed in the works, they are the sole responsibility of the authors. The Editor, with the assistance of the Editorial Committee, reserves the right to suggest or request advisable or necessary modifications. They are accepted to publish original scientific papers, research results of interest that have not been published or sent to another journal for the same purpose.
The mention of trademarks of equipment, instruments or specific materials is for identification purposes, and there is no promotional commitment in relation to them, neither by the authors nor by the publisher.
References
Appendini, C. M.; Torres, A.; Salles, P.; López, J. & Tonatiuh, E. 2014. “Wave Climate and Trends for the Gulf of Mexico: A 30-Yr Wave Hindcast”. Journal of Climate, 27(4): 1619–1632, DOI: 10.1175/JCLI-D-13-00206.1.
Appendini, C.; Ruiz, P.; Marsooli, R. & Cerezo, R. 2025. “Assessing the effects of climate change on the Gulf of Mexico wave climate using the COWCLIP framework and the PRECIS Regional Climate Model”. Ocean Modelling, 194: 102486, DOI: 10.1016/j.ocemod.2024.102486.
Badriana, M. R. & Lee, H. S. 2023. “Multimodel Ensemble Projections of Wave Climate in the Western North Pacific Using CMIP6 Marine Surface Winds”. Journal of Marine Science and Engineering, 9(8): 835–851, DOI: 10.3390/jmse9080835.
Baghanian, S. & Alizadeh, M. J. 2022. “Wave climate projection in the Persian Gulf: An ensemble of statistically downscaled CMIP6-GCMs”. Ocean Engineering, 266: 112821–112837, DOI: 10.1016/j.oceaneng.2022.112821.
Camus, P.; Losada, I., J.; Izaguirre, C.; Menéndez, M. & Pérez, J. 2017. “Statistical wave climate projections for coastal impact assessments”. Earth´s Future, 5(3): 918–933, DOI: 10.1002/2017EF000609.
Cannon, A., J.; Sobie, S., R. & Murdock, T., Q. 2015. “Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?”. Journal of Climate, 28(13): 6938–6959, DOI: 10.1175/JCLI-D-14-00754.1.
Cracknell, A., P. & Varostos, C., A. 2022. Understanding Global Climate Change. Modelling the Climatic System and Human Impacts. Second Edition ed., Boca Raton, US: Taylor & Francis Group, 427 p., ISBN: 978-0-429-20332-9.
deCastro, M.; Rusu, L.; Arguilé, B.; Ribeiro, A.; Costoya, X.; Carvalho, D. & Gómez, M. 2024. “Different approaches to analyze the impact of future climate change on the exploitation of wave energy”. Renewable Energy, 220: 119569–119591, DOI: 10.1016/j.renene.2023.119569.
Dobrynin, M.; Murawsky, J.; Baehr, J. & Ilyina, T. 2015. “Detection and Attribution of Climate Change Signal in Ocean Wind Waves”. Journal of Climate, 28(3): 1578–1591, DOI: 10.1175/JCLI-D-13-00664.1.
Gidden, M. J. and collaborators 2019. “Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century”. Geoscientific Model Development, 12(4): 1443–1475, DOI: 10.5194/gmd-12-1443-2019.
Hemer, M. A.; Wang, X. L.; Weisse, R. & Swail, V. R. 2012. “Advancing Wind-Waves Climate Science: The COWCLIP Project”. Bulletin of the American Meteorological Society, 93(6): 791–796, DOI: 10.1175/BAMS-D-11-00184.1.
Hernández, M.; Martínez, C. A. & Marzo, O. 2014. “Consequences of sea level variability and sea level rise for Cuban territory”. In: Proceedings of the International Association Hydrological Sciences, Gothenburg, Sweden, pp. 22–27, DOI: 10.5194/piahs-365-22-2015.
Hidalgo, A. & Mitrani, I. 2024. “Projection of Significant Wave Height in Gulf of Mexico”. In: Torres, Y., Beltran, A. M., Felix, M., Peralta, E. & Larios, D. F. (eds.), Recent Advances and Emerging Challenges in STEM. SPEMEPS 2023, (ser. Springer Proceedings in Materials), vol. 50, Gewerbestrasse, Switzerland: Springer Nature Switzerland AG, pp. 526–536, ISBN: 978-3-031-64106-0, Available:, [Consulted: August 12, 2024].
Hidalgo, A.; Mitrani, I. & Pérez, G. 2023. “Detección de cambios en la altura de la ola significativa en las costas cubanas”. Revista Cubana de Meteorología, 29(3): 1–11.
Hidalgo, A.; Mitrani, I. & Vichot, A. 2024. “Projected wind and waves around the Cuban archipelago using a multimodel ensemble”. Theoretical and Applied Climatology, 155(8): 7663–7677, DOI: 10.1007/s00704-024-05093-4.Iturralde, M., A. & Serrano, H. (eds.). 2015. Peligros y vulnerabilidades de la zona marino-costera de Cuba: estado actual y perspectivas ante el cambio climático hasta el 2100. La Habana: Editorial Academia, 72 p., ISBN: 978-959-270-338-4.
Kaur, S.; Kumar, P.; Min, S. K.; Krishnan, A. & Wang, X. L. 2023. “Evaluation of COWCLIP2.0 Ocean wave extreme indices over the Indian Ocean”. Climate Dynamics, 61: 5747–5765, DOI: 10.1007/s00382-023-06882-9.
Krishnan, A.; Bhaskaran, P. K. & Kumar, P. 2021. “CMIP5 model performance of significant wave heights over the Indian Ocean using COWCLIP datasets”. Theoretical and Applied Climatology, 145(1): 377–392, ISSN: 1434-4483, DOI: 10.1007/s00704-021-03642-9.
Lemos, G.; Menendez, M.; Semedo, A.; Camus, P.; Hemer, M.; Dobrynin, M. & Miranda, P. 2020. “On the need of bias correction methods for wave climate projections”. Global and Planetary Change, 186: 103109–103129, DOI: 10.1016/j.gloplacha.2019.103109.
Lemos, G.; Menendez, M.; Semedo, A.; Miranda, P. & Hemer, M. 2021. “On the decreases in North Atlantic significant wave heights from climate projections”. Climate Dynamics, 57: 2301–2324, DOI: 10.1007/s00382-021-05807-8.
Lemos, G.; Semedo, A.; Kumar, R.; Dobrynin, M.; Akpinar, A.; Kamranzad, B.; Bidlot, J. & Lobeto, H. 2023. “Performance evaluation of a global CMIP6 single forcing, multi wave model ensemble of wave climate simulations”. Ocean Modelling, 184: 102237–102258, DOI: 10.1016/j.ocemod.2023.102237.
Li, D.; Feng, J.; Xu, Z.; Yin, B.; Shi, H. & Qi, J. 2019. “Statistical Bias Correction for Simulated Wind Speeds Over CORDEX‐East Asia”. Earth and Space Science, 6(2): 200–211, DOI: 10.1029/2018EA000493.
Lira, A.; Berg, P.; Baquerizo, A. & Besio, G. 2023. “On the role of wave climate temporal variability in bias correction of GCM-RCM wave simulations”. Climate Dynamics, 61: 3541–3568, DOI: 10.1007/s00382-023-06756-0.
Losada, I. J.; Acevedo, A.; Camus, P.; Díaz, P.; Espejo, A.; Menéndez, M.; Menéndez, P.; Ramírez, M.; Toimil, A.; Torres, S.; Maza, M. E. & Fuentes, M. 2018. Reconstrucción histórica y proyecciones del efecto del cambio climático sobre el oleaje en la costa de Cuba. (ser. Efectos del cambio climático en la costa de América Latina y el Caribe), Santiago de Chile: Comisión Económica para América Latina y el Caribe (CEPAL), 63 p., Disponible en:, [Consultado: 17 de febrero de 2022].
Maraun, D. and Widmann, M. 2018. Statistical Downscaling and Bias Correction for Climate Research. Cambridge, UK: Cambridge University Press, 341 p., ISBN: 978-1-107-06605-2.
Meucci, A.; Young, I. R.; Hemer, M.; Kirezci, E. & Ranasinghe, R. 2020. “Projected 21st century changes in extreme wind-wave events”. Science Advances, 6: eaaz7295, DOI: 10.1126/sciadv.aaz7295.
Meucci, A.; Young, I. R.; Trenham, C. & Hemer, M. 2024. “An 8-model ensemble of CMIP6- derived ocean surface wave climate”. Scientific Data, 11: 100–112, DOI: 10.1038/s41597-024-02932-x.
Mohan, S. & Bhaskaran, P. K. 2019. “Evaluation of CMIP5 climate model projections for surface wind speed over the Indian Ocean region”. Climate Dynamics, 53: 5415–5435, DOI: 10.1007/s00382-019-04874-2.
Morim, J.; Vitousek, S.; Hemer, M.; Reguero, B.; Erikson, L.; Casas-Prat, M.; Wang, X. L.; Semedo, A.; Mori, N.; Shimura, T.; Mentaschi, L. & Timmermans, B. 2021. “Global-scale changes to extreme ocean wave events due to anthropogenic warming”. Environmental Research Letters, 16(7): 074056–0740565, DOI: 10.1088/1748-9326/ac1013.
Noël, T.; Loukos, H.; Defrance, D.; Vrac, M. & Levavasseur, G. 2021. “A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments”. Data in Brief, 35: 106900–106916, DOI: 10.1016/j.dib.2021.106900.
Parker, K. & Hill, D. F. 2017. “Evaluation of bias correction methods for wave modeling output”. Ocean Modelling, 110: 52–65, DOI: 10.1016/j.ocemod.2016.12.008.
Pérez, J.; Menéndez, M.; Méndez, F., J. & Losada, I., J. 2014. “Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region”. Climate Dynamics, 51: 2663–2680, DOI: 10.1007/s00382-014-2078-8.
Pérez, R. 2019. “Climbing the sea level in Cuba due to Climate Change”. Revista Cubana de Meteorología, 25(1): 76–83.
Planos, E., O. & Gutiérrez, T., L. (eds.). 2020. Tercera Comunicación Nacional a la Convención Marco de las Naciones Unidas sobre Cambio Climático. La Habana: AMA Sello Editorial, 402 p., ISBN: 978-959-300-170-0.
Planos, E., O.; Rivero, R. & Guevara, V. (eds.). 2013. Impactos del Cambio Climático y Medidas de Adaptación en Cuba. La Habana: Editorial AMA, 430 p., ISBN: 978-959-300-039-0.
Ramon, J.; Lledó, L.; Torralba, V.; Soret, A. & Doblas, F. J. 2019. “What global reanalysis best represents near-surface winds?”. Quarterly Journal of the Royal Meteorological Society, 145(724): 3236–3251, DOI: 10.1002/qj.3616.
Sardana, D.; Kumar, P.; Bhaskaran, P. K. & Nair, T. M. B. 2022. “The projected changes in extreme wave height indices over the Indian Ocean using COWCLIP2.0 datasets”. Climate Dynamics, 61: 255–269, DOI: 10.1007/s00382-022-06579-5.
Taniguchi, K. 2019. “Variations in Winter Ocean Wave Climate in the Japan Sea under the Global Warming Condition”. Journal of Marine Science and Engineering, 7(5): 150–166, DOI: 10.3390/jmse7050150.
Torma, C.; Giorgi, F. & Coppola, E. 2015. “Added value of regional climate modeling over areas characterized by complex terrain Precipitation over the Alps”. Journal of Geophysical Research: Atmospheres, 120(9): 3957–3972, DOI: 10.1002/2014JD022781.
Wang, X. L.; Feng, Y. & Swail, V. R. 2014. “Changes in global ocean wave heights as projected using multimodel CMIP5 simulations”. Geophysical Research Letters, 41: 1026–1034, DOI: 10.1002/2013GL058650.
Wilks, D. S. 2019. Statistical methods in the atmospheric sciences. Fourth Edition ed., Oxford, United Kingdom: Elsevier/Academic Press, 818 p., ISBN: 978-0-12-385022.
Yadav, A.; Kumar, P.; Bhaskaran, P., K.; Hisaki, Y. & Ranji 2024. “Evaluation of ocean wave power utilizing COWCLIP 2.0 datasts: a CMIP5 model assessment”. Climate Dynamics, 62: 9447–9468, DOI: 10.1007/s00382-024-07402-z.
Appendini, C.; Ruiz, P.; Marsooli, R. & Cerezo, R. 2025. “Assessing the effects of climate change on the Gulf of Mexico wave climate using the COWCLIP framework and the PRECIS Regional Climate Model”. Ocean Modelling, 194: 102486, DOI: 10.1016/j.ocemod.2024.102486.
Badriana, M. R. & Lee, H. S. 2023. “Multimodel Ensemble Projections of Wave Climate in the Western North Pacific Using CMIP6 Marine Surface Winds”. Journal of Marine Science and Engineering, 9(8): 835–851, DOI: 10.3390/jmse9080835.
Baghanian, S. & Alizadeh, M. J. 2022. “Wave climate projection in the Persian Gulf: An ensemble of statistically downscaled CMIP6-GCMs”. Ocean Engineering, 266: 112821–112837, DOI: 10.1016/j.oceaneng.2022.112821.
Camus, P.; Losada, I., J.; Izaguirre, C.; Menéndez, M. & Pérez, J. 2017. “Statistical wave climate projections for coastal impact assessments”. Earth´s Future, 5(3): 918–933, DOI: 10.1002/2017EF000609.
Cannon, A., J.; Sobie, S., R. & Murdock, T., Q. 2015. “Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?”. Journal of Climate, 28(13): 6938–6959, DOI: 10.1175/JCLI-D-14-00754.1.
Cracknell, A., P. & Varostos, C., A. 2022. Understanding Global Climate Change. Modelling the Climatic System and Human Impacts. Second Edition ed., Boca Raton, US: Taylor & Francis Group, 427 p., ISBN: 978-0-429-20332-9.
deCastro, M.; Rusu, L.; Arguilé, B.; Ribeiro, A.; Costoya, X.; Carvalho, D. & Gómez, M. 2024. “Different approaches to analyze the impact of future climate change on the exploitation of wave energy”. Renewable Energy, 220: 119569–119591, DOI: 10.1016/j.renene.2023.119569.
Dobrynin, M.; Murawsky, J.; Baehr, J. & Ilyina, T. 2015. “Detection and Attribution of Climate Change Signal in Ocean Wind Waves”. Journal of Climate, 28(3): 1578–1591, DOI: 10.1175/JCLI-D-13-00664.1.
Gidden, M. J. and collaborators 2019. “Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century”. Geoscientific Model Development, 12(4): 1443–1475, DOI: 10.5194/gmd-12-1443-2019.
Hemer, M. A.; Wang, X. L.; Weisse, R. & Swail, V. R. 2012. “Advancing Wind-Waves Climate Science: The COWCLIP Project”. Bulletin of the American Meteorological Society, 93(6): 791–796, DOI: 10.1175/BAMS-D-11-00184.1.
Hernández, M.; Martínez, C. A. & Marzo, O. 2014. “Consequences of sea level variability and sea level rise for Cuban territory”. In: Proceedings of the International Association Hydrological Sciences, Gothenburg, Sweden, pp. 22–27, DOI: 10.5194/piahs-365-22-2015.
Hidalgo, A. & Mitrani, I. 2024. “Projection of Significant Wave Height in Gulf of Mexico”. In: Torres, Y., Beltran, A. M., Felix, M., Peralta, E. & Larios, D. F. (eds.), Recent Advances and Emerging Challenges in STEM. SPEMEPS 2023, (ser. Springer Proceedings in Materials), vol. 50, Gewerbestrasse, Switzerland: Springer Nature Switzerland AG, pp. 526–536, ISBN: 978-3-031-64106-0, Available:
Hidalgo, A.; Mitrani, I. & Pérez, G. 2023. “Detección de cambios en la altura de la ola significativa en las costas cubanas”. Revista Cubana de Meteorología, 29(3): 1–11.
Hidalgo, A.; Mitrani, I. & Vichot, A. 2024. “Projected wind and waves around the Cuban archipelago using a multimodel ensemble”. Theoretical and Applied Climatology, 155(8): 7663–7677, DOI: 10.1007/s00704-024-05093-4.Iturralde, M., A. & Serrano, H. (eds.). 2015. Peligros y vulnerabilidades de la zona marino-costera de Cuba: estado actual y perspectivas ante el cambio climático hasta el 2100. La Habana: Editorial Academia, 72 p., ISBN: 978-959-270-338-4.
Kaur, S.; Kumar, P.; Min, S. K.; Krishnan, A. & Wang, X. L. 2023. “Evaluation of COWCLIP2.0 Ocean wave extreme indices over the Indian Ocean”. Climate Dynamics, 61: 5747–5765, DOI: 10.1007/s00382-023-06882-9.
Krishnan, A.; Bhaskaran, P. K. & Kumar, P. 2021. “CMIP5 model performance of significant wave heights over the Indian Ocean using COWCLIP datasets”. Theoretical and Applied Climatology, 145(1): 377–392, ISSN: 1434-4483, DOI: 10.1007/s00704-021-03642-9.
Lemos, G.; Menendez, M.; Semedo, A.; Camus, P.; Hemer, M.; Dobrynin, M. & Miranda, P. 2020. “On the need of bias correction methods for wave climate projections”. Global and Planetary Change, 186: 103109–103129, DOI: 10.1016/j.gloplacha.2019.103109.
Lemos, G.; Menendez, M.; Semedo, A.; Miranda, P. & Hemer, M. 2021. “On the decreases in North Atlantic significant wave heights from climate projections”. Climate Dynamics, 57: 2301–2324, DOI: 10.1007/s00382-021-05807-8.
Lemos, G.; Semedo, A.; Kumar, R.; Dobrynin, M.; Akpinar, A.; Kamranzad, B.; Bidlot, J. & Lobeto, H. 2023. “Performance evaluation of a global CMIP6 single forcing, multi wave model ensemble of wave climate simulations”. Ocean Modelling, 184: 102237–102258, DOI: 10.1016/j.ocemod.2023.102237.
Li, D.; Feng, J.; Xu, Z.; Yin, B.; Shi, H. & Qi, J. 2019. “Statistical Bias Correction for Simulated Wind Speeds Over CORDEX‐East Asia”. Earth and Space Science, 6(2): 200–211, DOI: 10.1029/2018EA000493.
Lira, A.; Berg, P.; Baquerizo, A. & Besio, G. 2023. “On the role of wave climate temporal variability in bias correction of GCM-RCM wave simulations”. Climate Dynamics, 61: 3541–3568, DOI: 10.1007/s00382-023-06756-0.
Losada, I. J.; Acevedo, A.; Camus, P.; Díaz, P.; Espejo, A.; Menéndez, M.; Menéndez, P.; Ramírez, M.; Toimil, A.; Torres, S.; Maza, M. E. & Fuentes, M. 2018. Reconstrucción histórica y proyecciones del efecto del cambio climático sobre el oleaje en la costa de Cuba. (ser. Efectos del cambio climático en la costa de América Latina y el Caribe), Santiago de Chile: Comisión Económica para América Latina y el Caribe (CEPAL), 63 p., Disponible en:
Maraun, D. and Widmann, M. 2018. Statistical Downscaling and Bias Correction for Climate Research. Cambridge, UK: Cambridge University Press, 341 p., ISBN: 978-1-107-06605-2.
Meucci, A.; Young, I. R.; Hemer, M.; Kirezci, E. & Ranasinghe, R. 2020. “Projected 21st century changes in extreme wind-wave events”. Science Advances, 6: eaaz7295, DOI: 10.1126/sciadv.aaz7295.
Meucci, A.; Young, I. R.; Trenham, C. & Hemer, M. 2024. “An 8-model ensemble of CMIP6- derived ocean surface wave climate”. Scientific Data, 11: 100–112, DOI: 10.1038/s41597-024-02932-x.
Mohan, S. & Bhaskaran, P. K. 2019. “Evaluation of CMIP5 climate model projections for surface wind speed over the Indian Ocean region”. Climate Dynamics, 53: 5415–5435, DOI: 10.1007/s00382-019-04874-2.
Morim, J.; Vitousek, S.; Hemer, M.; Reguero, B.; Erikson, L.; Casas-Prat, M.; Wang, X. L.; Semedo, A.; Mori, N.; Shimura, T.; Mentaschi, L. & Timmermans, B. 2021. “Global-scale changes to extreme ocean wave events due to anthropogenic warming”. Environmental Research Letters, 16(7): 074056–0740565, DOI: 10.1088/1748-9326/ac1013.
Noël, T.; Loukos, H.; Defrance, D.; Vrac, M. & Levavasseur, G. 2021. “A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments”. Data in Brief, 35: 106900–106916, DOI: 10.1016/j.dib.2021.106900.
Parker, K. & Hill, D. F. 2017. “Evaluation of bias correction methods for wave modeling output”. Ocean Modelling, 110: 52–65, DOI: 10.1016/j.ocemod.2016.12.008.
Pérez, J.; Menéndez, M.; Méndez, F., J. & Losada, I., J. 2014. “Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region”. Climate Dynamics, 51: 2663–2680, DOI: 10.1007/s00382-014-2078-8.
Pérez, R. 2019. “Climbing the sea level in Cuba due to Climate Change”. Revista Cubana de Meteorología, 25(1): 76–83.
Planos, E., O. & Gutiérrez, T., L. (eds.). 2020. Tercera Comunicación Nacional a la Convención Marco de las Naciones Unidas sobre Cambio Climático. La Habana: AMA Sello Editorial, 402 p., ISBN: 978-959-300-170-0.
Planos, E., O.; Rivero, R. & Guevara, V. (eds.). 2013. Impactos del Cambio Climático y Medidas de Adaptación en Cuba. La Habana: Editorial AMA, 430 p., ISBN: 978-959-300-039-0.
Ramon, J.; Lledó, L.; Torralba, V.; Soret, A. & Doblas, F. J. 2019. “What global reanalysis best represents near-surface winds?”. Quarterly Journal of the Royal Meteorological Society, 145(724): 3236–3251, DOI: 10.1002/qj.3616.
Sardana, D.; Kumar, P.; Bhaskaran, P. K. & Nair, T. M. B. 2022. “The projected changes in extreme wave height indices over the Indian Ocean using COWCLIP2.0 datasets”. Climate Dynamics, 61: 255–269, DOI: 10.1007/s00382-022-06579-5.
Taniguchi, K. 2019. “Variations in Winter Ocean Wave Climate in the Japan Sea under the Global Warming Condition”. Journal of Marine Science and Engineering, 7(5): 150–166, DOI: 10.3390/jmse7050150.
Torma, C.; Giorgi, F. & Coppola, E. 2015. “Added value of regional climate modeling over areas characterized by complex terrain Precipitation over the Alps”. Journal of Geophysical Research: Atmospheres, 120(9): 3957–3972, DOI: 10.1002/2014JD022781.
Wang, X. L.; Feng, Y. & Swail, V. R. 2014. “Changes in global ocean wave heights as projected using multimodel CMIP5 simulations”. Geophysical Research Letters, 41: 1026–1034, DOI: 10.1002/2013GL058650.
Wilks, D. S. 2019. Statistical methods in the atmospheric sciences. Fourth Edition ed., Oxford, United Kingdom: Elsevier/Academic Press, 818 p., ISBN: 978-0-12-385022.
Yadav, A.; Kumar, P.; Bhaskaran, P., K.; Hisaki, Y. & Ranji 2024. “Evaluation of ocean wave power utilizing COWCLIP 2.0 datasts: a CMIP5 model assessment”. Climate Dynamics, 62: 9447–9468, DOI: 10.1007/s00382-024-07402-z.