Analysis of climate change scenarios using CMIP6 models in Pernambuco, Brazil

Diego Cezar dos Santos Araujo; Suzana Maria Gico Lima Montenegro; Samara Fernanda da Silva; Vanine Elane Menezes de Farias; Arivânia Bandeira Rodrigues

doi:10.5327/Z2176-94781868

Authors

Diego Cezar dos Santos Araujo Federal University of Pernambuco (UFPE) - Brazil https://orcid.org/0000-0002-8969-260X
Suzana Maria Gico Lima Montenegro Federal University of Pernambuco (UFPE) - Brazil https://orcid.org/0000-0002-2520-5761
Samara Fernanda da Silva Federal University of Western Bahia (UFOB) - Brazil https://orcid.org/0000-0002-8853-8993
Vanine Elane Menezes de Farias Federal University of Pernambuco (UFPE) - Brazil https://orcid.org/0000-0002-2339-7528
Arivânia Bandeira Rodrigues Federal University of Pernambuco (UFPE) - Brazil https://orcid.org/0000-0003-0452-0006

DOI:

https://doi.org/10.5327/Z2176-94781868

Keywords:

temperature; precipitation; climate variability; shared socioeconomic pathways; water resources.

Abstract

Monitoring the effects of climate change is essential due to the ongoing increase in extreme drought and flood events, primarily driven by changes in key variables such as precipitation and temperature. In this study, data from eight Coupled Model Intercomparison Project Phase 6 (CMIP6) models were used to assess temperature and precipitation anomalies in the state of Pernambuco, Brazil, for the period 2041 to 2100, considering two different climate scenarios (SSP245 and SSP585). The projected data were compared with WorldClim historical climatological data between 1970 and 2000. Due to the significant spatial variability of annual precipitation in Pernambuco, ranging from 400 to 2,200 mm, the state was evaluated considering its territory in total and also in two distinct climatic regions (Sertão/Agreste and Zona da Mata). An increase in temperature is projected, even in the least pessimistic scenario (SSP245) with an increment of 1.64°C from 2041 to 2060. During the same period, an increase of 2.10°C is expected in the SSP585 scenario. For the period from 2081 to 2100, the models indicate increases of 2.45 and 4.53°C, respectively. Precipitation will decrease in all scenarios and regions of Pernambuco, with a reduction of up to 227.24 mm year^-1 in the Zona da Mata between 2081 and 2100 in the SSP585 scenario. These potential changes pose imminent threats to water resources, agriculture, biodiversity, and the population, demanding proactive measures from policymakers and stakeholders to mitigate these effects.

Downloads

Download data is not yet available.

References

Agência Nacional de Águas e Saneamento Básico (ANA), 2019. Plano Nacional de Segurança Hídrica – PNSH. Brasília: ANA (Accessed September 09, 2023) at:. https://arquivos.ana.gov.br/pnsh/pnsh.pdf

Agência Nacional de Águas e Saneamento Básico (ANA), 2024. Impacto da Mudança Climática nos Recursos Hídricos no Brasil. Brasília: ANA, 96 p (Accessed September 09, 2023) at:. https://metadados.snirh.gov.br/geonetwork/srv/api/records/31604c98-5bbe-4dc9-845d-998815607b33/attachments/Mudancas_Climaticas_25012024.pdf

Almazroui, M.; Ashfaq, M.; Islam, M.N.; Rashid, I.U.; Kamil, S.; Abid, M.A.; O’Brien, E.; Ismail, M.; Reboita, M.S.; Sörensson, A.A.; Arias, P.A.; Alves, L.M.; Tippett, M.K.; Saeed, S.; Haarsma, R.; Doblas-Reyes, F.J.; Saeed, F.; Kucharski, F.; Nadeem, I.; Silva-Vidal, Y.; Rivera, J.A.; Ehsan, M.A.; Martínez-Castro, D.; Muñoz, Á.G.; Ali, M.A.; Coppola, E.; Sylla, M.B., 2021. Assessment of cmip6 performance and projected temperature and precipitation changes over South America. Earth Systems and Environment, v. 5, (2), 155-183. https://doi.org/10.1007/s41748-021-00233-6

Andrade, C.W.L.; Montenegro, S.M.G.L.; Montenegro, A.A.A.; Lima, J.R.S.; Srinivasan, R.; Jones; C.A., 2021. Climate change impact assessment on water resources under RCP scenarios: a case study in Mundaú River Basin, Northeastern Brazil. International Journal of Climatology, v. 41, (S1), 1045-1061. https://doi.org/10.1002/joc.6751

Araujo, D.C.S.; Montenegro, S.M.G.L.; Corbari, C.; Viana, J.F.S., 2021. Calibration of FEST-EWB hydrological model using remote sensing data in a climate transition region in Brazil. Hydrological Sciences Journal, v. 66, (3), 513-524. https://doi.org/10.1080/02626667.2021.1881100

Ballarin, A.S.; Sone, J.S.; Gesualdo, G.C.; Schwamback, D.; Reis, A.; Almagro, A.; Wendland, E.C., 2023. CLIMBra - Climate Change Dataset for Brazil. Scientific Data, v. 10, (1), 47. https://doi.org/10.1038/s41597-023-01956-z

Bezerra, A.C.; da Costa, S.A.T.; da Silva, J.L.B.; Araújo, A.M.Q.; Moura, G.B.A.; Lopes, P.M.O.; Nascimento, C.R., 2021. Annual rainfall in Pernambuco, Brazil: Regionalities, regimes, and time trends. Revista Brasileira de Meteorologia, v. 36, (3), 403-414. https://doi.org/10.1590/0102-77863630129

Centro de Gestão e Estudos Estratégicos (CGEE), 2016. Desertificação, degradação da terra e secas no Brasil. Brasília, DF: CGEE. 252 p (Accessed September 17, 2023) at:. http://www.cgee.org.br.

Cos, J.; Doblas-Reyes, F.; Jury, M.; Marcos, R.; Bretonnière, P.A.; Samsó, M., 2022. The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections. Earth System Dynamics, v. 13, (1), 321-340. https://doi.org/10.5194/esd-13-321-2022

Dantas, L.G.; dos Santos, C.A.C.; Santos, C.A.G.; Martins, E.S.P.R.; Alves, L.M., 2022. Future changes in temperature and precipitation over Northeastern Brazil by CMIP6 Model. Water (Switzerland), v. 14, (24), 4118. https://doi.org/10.3390/w14244118

Eyring, V.; Bony, S.; Meehl, G.A.; Senior, C.A.; Stevens, B.; Stouffer, R.J.; Taylor, K.E., 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, v. 9, (5), 1937-1958. https://doi.org/10.5194/gmd-9-1937-2016

Fick, S.E.; Hijmans, R.J., 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, v. 37, (12), 4302-4315. https://doi.org/10.1002/joc.5086

Guimarães, S.O.; Costa, A.A.; Vasconcelos Júnior, F.C.; da Silva, E.M.; Sales, D.C.; de Araújo Júnior, L.M.; de Souza, S.G., 2016. Projeções de mudanças climáticas sobre o Nordeste Brasileiro dos modelos do CMIP5 e do CORDEX. Revista Brasileira de Meteorologia, v. 31, (3), 337-365. https://doi.org/10.1590/0102-778631320150150

Jayawardhana, W.G.N.N.; Chathurange, V.M.I., 2020. Investigate the sensitivity of the satellite-based agricultural drought indices to monitor the drought condition of paddy and introduction to enhanced multi-temporal drought indices. Journal of Remote Sensing & GIS, v. 9, (272). https://doi.org/10.35248/2469-4134.20.9.272

Kamruzzaman, M.; Shahid, S.; Islam, A.T.; Hwang, S.; Cho, J.; Zaman, M.A.U.; Ahmed, M.; Rahman, M.M.; Hossain, M.B., 2021. Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study. Theoretical and Applied Climatology, v. 145, (3-4), 1385-1406. https://doi.org/10.1007/s00704-021-03691-0

Kunreuther, H.; Heal, G.; Allen, M.; Edenhofer, O.; Field, C.B.; Yohe, G., 2013. Risk management and climate change. In Nature Climate Change, v. 3, (5), 447-450. https://doi.org/10.1038/nclimate1740

Marques, M.T.A.; Kovalski, M.L.; Perez, G.M.P.; Martin, T.C.M.; Barbosa, E.L.S.Y.; Ribeiro, P.A.S.M.; Valdes, R.H., 2024. Data-driven discovery of mechanisms underlying present and nearfuture precipitation changes and variability in Brazil. EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2024-48

Marengo, J.A.; Torres, R.R.; Alves, L.M., 2017. Drought in Northeast Brazil - past, present, and future. Theoretical and Applied Climatology, v. 129, (3-4), 1189-1200. https://doi.org/10.1007/s00704-016-1840-8

Marengo, J.A.; Camarinha, P.I.; Alves, L.M.; Diniz, F.; Betts, R.A., 2021. Extreme rainfall and hydro-geo-meteorological disaster risk in 1.5, 2.0, and 4.0°c global warming scenarios: an analysis for Brazil. Frontiers in Climate, v. 3, 610433. https://doi.org/10.3389/fclim.2021.610433

Marengo, J.A.; Galdos, M.V.; Challinor, A.; Cunha, A.P.; Marin, F.R; Vianna, M.S.; Alvala, R.C.S.; Alves, L.M.; Moraes, O.L.; Bender, F., 2022. Drought in Northeast Brazil: A review of agricultural and policy adaptation options for food security. Climate Resilience and Sustainability, v. 1, 17. https://doi.org/10.1002/cli2.17

Marengo, J.A.; Alcantara, E.; Cunha, A.P.; Seluchi, M.; Nobre, C. A.; Dolif, G.; Goncalves, D.; Assis Dias, M.; Cuartas, L.A.; Bender, F.; Ramos, A.M.; Mantovani, J.R.; Alvalá, R.C.; Moraes, O.L., 2023. Flash floods and landslides in the city of Recife, Northeast Brazil after heavy rain on May 25–28, 2022: Causes, impacts, and disaster preparedness. Weather and Climate Extremes, v. 39, 100545. https://doi.org/10.1016/j.wace.2022.100545

Medeiros, F.J.; Oliveira, C.P.; Avila-Diaz, A., 2022. Evaluation of extreme precipitation climate indices and their projected changes for Brazil: From CMIP3 to CMIP6. Weather and Climate Extremes, v. 38, 100511. https://doi.org/10.1016/j.wace.2022.100511

Nandgude, N.; Singh, T.P.; Nandgude, S.; Tiwari, M., 2023. Drought prediction: a comprehensive review of different drought prediction models and adopted technologies. Sustainability, v. 15, (15), 11684. https://doi.org/10.3390/su151511684

Reboita, M.S.; Kuki, C.A.C.; Marrafon; V.H.; de Souza, C.A.; Ferreira, G.W.S.; Teodoro, T.; Lima, J.W.M., 2022. South America climate change revealed through climate indices projected by GCMs and Eta-RCM ensembles. Climate Dynamics, v. 58, 459-485. https://doi.org/10.1007/s00382-021-05918-2

Rossato, L.; Marengo, J.A.; Angelis, C.F.; Pires, L.B.M.; Mendiondo, E.M., 2017. Impact of soil moisture over Palmer Drought Severity Index and its future projections in Brazil. Brazilian Journal of Water Resources, v. 22, e36. https://doi.org/10.1590/2318-0331.0117160045

Saboia, M.A.M.; Souza Filho, F.A.; Helfer, F.; Rolim, L.Z.R., 2020. Robust strategy for assessing the costs of urban drainage system designs under climate change scenarios. Journal of Water Resources Planning and Management, v. 146, (11), 05020022. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001281

Santos; J.O.; Santos, R.M.S.; Fernandes, A.A.; Souso, J.S.; Borges, M.G.B.; Ferreira, R.T.F.V.; Salgado, A.B., 2013. Os impactos produzidos pelas mudanças climáticas. ACSA - Agropecuária Científica No Seminário, v. 9, (1), 9-16. https://doi.org/10.30969/acsa.v9i1.259

Santos, F.L.S.; Vasconcelos, V.; de Jesus, K.; Couto Junior, A.F.; Neves, G.; Sena-Souza, J.P.; Sampaio, E.; Ometto, J.; Menezes, R.; Nardoto, G.B., 2022. Climatic control effect on the soil nitrogen isotopic composition in Alisols across the physiographic regions of Pernambuco State, Northeast Brazil. Geoderma Regional, v. 30, e00565. https://doi.org/10.1016/j.geodrs.2022.e00565

Silva, L.A.; Silva, C.R.; Souza, C.M.; Bolfe, E.L.; Souza, J.P.; Leite, M.E., 2023. Mapping of aridity and its connections with climate classes and climate desertification in future scenarios - Brazilian semi-arid region. 2023. Sociedade & Natureza, v. 35, e67666. https://doi.org/10.14393/SN-v35-2023-67666x

Silva, T.R.B.F.; Santos, C.A.C.; Silva, D J.F.; Santos, C.A.G.; da Silva, R.M.; de Brito, J.I.V., 2022. Climate indices‐based analysis of rainfall spatiotemporal variability in Pernambuco State, Brazil. Water, v. 14, 2190. https://doi.org/10.3390/w14142190

Silveira, C.S.; Souza Filho, F.A.; Costa, A.A.; Cabral, S.L., 2013. Avaliação de desempenho dos modelos do CMIP5 quanto à representação dos padrões de variação da precipitação no século XX sobre a região Nordeste do Brasil, Amazônia e bacia do prata e análise das projeções para o cenário RCP 8.5. Revista Brasileira de Meteorologia, v. 28, (3), 317-330. https://doi.org/10.1590/S0102-77862013000300008

Soares, A.S.D.; da Paz, A.R.; Piccilli, D.G.A., 2016. Avaliação das estimativas de chuva do satélite TRMM no Estado da Paraíba. Revista Brasileira de Recursos Hídricos, v. 21, (2), 288-299. http://dx.doi.org/10.21168/rbrh.v21n2.p288-299

Souza, A.G.G.; Neto, A.R.; Rossato, L.; Alvalá, R.C.S.; Souza, L.L., 2018. Use of SMOS L3 soil moisture data: validation and drought assessment for Pernambuco State, Northeast Brazil. Remote Sensing, v. 10, (8), 1314. https://doi.org/10.3390/rs10081314

Tebaldi, C.; Debeire, K.; Eyring, V.; Fischer, E.; Fyfe, J.; Friedlingstein, P.; Knutti, R.; Lowe, J.; O'Neill, B.; Sanderson, B.; van Vuuren, D.; Riahi, K.; Meinshausen, M.; Nicholls, Z.; Tokarska, K.B.; Hurtt, G.; Kriegler, E.; Lamarque, J.-F.; Meehl, G.; Moss, R.; Bauer, S.E.; Boucher, O.; Brovkin, V.; Byun, Y.-H.; Dix, M.; Gualdi, S.; Guo, H.; John, J.G.; Kharin, S.; Kim, Y.; Koshiro, T.; Ma, L.; Olivié, D.; Panickal, S.; Qiao, F.; Rong, X.; Rosenbloom, N.; Schupfner, M.; Séférian, R.; Sellar, A.; Semmler, T.; Shi, X.; Song, Z.; Steger, C.; Stouffer, R.; Swart, N.; Tachiiri, K.; Tang, Q.; Tatebe, H.; Voldoire, A.; Volodin, E.; Wyser, K.; Xin, X.; Yang, S.; Yu, Y.; Ziehn, T., 2021. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth System Dynamics, v. 12, 253-293. https://doi.org/10.5194/esd-12-253-2021

Wu, Y.; Miao, C.; Sun, Y.; AghaKouchak, A.; Shen, C.; Fan, X., 2021. Global observations and CMIP6 simulations of compound extremes of monthly temperature and precipitation. GeoHealth, v. 5, (5), e2021GH000390. https://doi.org/10.1029/2021GH000390