ANÁLISE DA VARIABILIDADE E TENDÊNCIAS MENSAIS, SAZONAIS E ANUAIS DA TEMPERATURA DO AR NO BRASIL
DOI:
https://doi.org/10.22409/GEOgraphia2026.v28i61.a59822Keywords:
Temperatura do Ar, Mudanças Climáticas, Brasil, Teste Mann–Kendall, tendênciasAbstract
A temperatura desempenha um papel importante na detecção das alterações climáticas provocadas pela urbanização e industrialização. Este estudo tem como objetivo verificar a variabilidade e tendências da temperatura média máxima anual, sazonal e mensal do ar superficial no Brasil durante o período 1960-2020. Três testes estatísticos, o teste de Pettitt, o teste sequencial de Mann-Kendall (teste SQ-MK) e o teste de classificação de Mann-Kendall (teste MK) foram utilizados para análise. Os resultados da análise de todo o período revelam um crescimento estatisticamente significativa em praticamente todos os meses e estações do ano. Observou-se também uma relação significativamente positiva para anos mais quentes, com o verão e a primavera significativamente mais quentes e o outono e o inverno ligeiramente mais quentes. Tendências positivas na temperatura média anual foram encontradas ao longo do período. Para toda a região, as temperaturas máximas médias aumentaram aproximadamente 0,012∘C por ano.
Downloads
References
ALMEIDA CT, OLIVEIRA-JÚNIOR JF, DELGADO RC, CUBO P, RAMOS MC (2017) Spatiotemporal rainfall and temperature trends through- out the Brazilian Legal Amazon, 1973–2013. Int J Climatol 37(4):2013–2026. https://doi.org/10.1002/joc.4831
ALVES, J.M.B.; VASCONCELOS JUNIOR, F.C.; CHAVES, R.R.; SILVA, E.M.; SERVAIN, J.; COSTA, A.A.; SOMBRA, S.S.; BARBOSA, A.C.B.; DOS SANTOS, A.C.S. (2016). Evaluation of the AR4 CMIP3 and the AR5 CMIP5 model and projections for precipitation in northeast Brazil. Frontiers in Earth Science, 4:4. https://doi.org/10.3389/feart.2016.00044
AVILA-DIAZ, A.; BENEZOLI, V.; JUSTINO, F.; TORRES, R.; WILSON, A. (2020). Assessing current and future trends of climate extremes across Brazil based on reanalyses and earth system model projections. Climate Dynamics, 55(5-6): 1403-1426. https://doi.org/10.1007/s00382-020-05333-z
CARVALHO A A; MONTENEGRO A A A; SILVA H P; LOPES L; MORAES J E F; SILVA T G F. (2020). Trends of rainfall and temperature in Northeast Brazil. Rev. bras. eng. agríc. ambient. 24 (1) • Jan 2020 • https://doi.org/10.1590/1807-1929/agriambi.v24n1p15-23
CHOU, S.C.; LYRA, A.; MOURÃO, C.; DERECZYNSKI, C.; PILOTTO, I.; GOMES, J.; BUSTAMANTE, J.; TAVARES, P.; SILVA, A.; RODRIGUES, D.; CAMPOS, D.; CHAGAS, D.; SUEIRO, G.; SIQUEIRA, G.; NOBRE, P.; MARENGO, J. 2014. Evaluation of the Eta Simulations Nested in Three Global Climate Models. American Journal of Climate Change, 3(5): 438-454. https://doi.org/10.4236/ajcc.2014.35039
COSTA, R.L.; BAPTISTA, G.M.M.; GOMES, H.B.; SILVA, F.D.S.; ROCHA JÚNIOR, R.L.; NEDEL, A.S. 2021. Analysis of future climate scenarios for northeastern Brazil and implications for human thermal comfort. Anais da Academia Brasileira de Ciencias, 93(1): 1-23. https://doi.org/10.1590/0001-3765202120190651
DA SILVA CRUZ, J.; BLANCO, C.J.C.; DE OLIVEIRA JÚNIOR, J.F. 2022. Modeling of land use and land cover change dynamics for future projection of the Amazon number curve. Science of the Total Environment, 811: 152348. https://doi.org/10.1016/j.scitotenv.2021.152348
DE ABREU, R.C.; TETT, S.F.B.; SCHURER, A.; ROCHA, H.R. 2019. Attribution of Detected Temperature Trends in Southeast Brazil. Geophysical Research Letters, 46(14): 8407-8414. https://doi.org/10.1029/2019GL083003
DERECZYNSKI, C.; CHOU, S.C.; LYRA, A.; SONDERMANN, M.; REGOTO, P.; TAVARES, P.; CHAGAS, D.; GOMES, J.L.; RODRIGUES, D.C.; SKANSI, M.L.M. 2020. Downscaling of climate extremes over South America - Part I: Model evaluation in the reference climate. Weather and Climate Extremes, 29: 100273. https://doi.org/10.1016/j.wace.2020.100273
DOHLER, R.E.; KLIPPEL, A.H.; XAVIER, A.C. 2016. Efeito das mudanças climáticas na demanda de irrigação na cultura do café co-nilon e do mamoeiro no Espírito Santo. Revista Agro@Mbiente On-Line, 10(1): 83-87. https://doi.org/10.18227/1982-8470ragro.v10i1.2961.
FAN, X.; MIAO, C.; DUAN, Q.; SHEN, C.; WU, Y. 2020. The Performance of CMIP6 Versus CMIP5 in Simulating Temperature Extremes Over the Global Land Surface. Journal of Geophysical Research: Atmospheres, 125(18): 1-16. https://doi.org/10.1029/2020JD033031
FRAGA, M.D., REIS, G.B., DA SILVA, D.D. et al.2020. Use of multivariate statistical methods to analyze the monitoring of surface water quality in the Doce River basin, Minas Gerais, Brazil. Environ Sci Pollut Res 27, 35303–35318 (2020). https://doi.org/10.1007/s11356-020-09783-0
FUNK, C.; PETERSON, P.; LANDSFELD, M.; PEDREROS, D.; VERDIN, J.; SHUKLA, S.; HUSAK, G.; ROWLAND, J.; HARRISON, L.; HOELL, A.; MICHAELSEN, J. 2015. The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Scientific Data, 2: 150066. https://doi.org/10.1038/sdata.2015.66.
GAJBHIYE, S., MESHRAM, C., SINGH, S.K., SRIVASTAVA, P.K. AND ISLAM, T. (2016) Precipitation trend analysis of Sindh River basin, India, from 102-year record (1901-2002). Atmospheric Science Letters, 17, 71– 77.
GHASEMI, A.R. (2015) Changes and trends in maximum, minimum and mean temperature series in Iran. Atmospheric Science Letters, 16, 366– 372. https://doi.org/10.1002/asl2.569
GONÇALVES F N E BACK A J. 2018. Analysis of space and seasonal variation and trends in the precipitation of the southern region of Brazil. Revista de Ciências Agrárias, 2018, 41(3): 592-602. http://dx.doi.org/10.19084/RCA17204
HARRIS I, JONES PD, OSBORN TJ, LISTER DH. (2014). Updated high resolution grids of monthly climatic observations-the CRU TS3.10 dataset. International Journal of Climatology 34: 623-642. https://doi.org/10.1002/joc.3711
HOFMANN, G.S.; CARDOSO, M.F.; ALVES, R.J.V.; WEBER, E.J.; BARBOSA, A.A.; DE TOLEDO, P.M.; PONTUAL, F.B.; SALLES, L.O.; HASENACK, H.; CORDEIRO, J.L.P.; AQUINO, F.E.; DE OLIVEIRA, L.F.B. 2021. The Brazilian Cerrado is becoming hotter and drier. Global Change Biology, 27(17): 4060-4073. https://doi.org/10.1111/gcb.15712
IBGE – Instituto Brasileiro de Geografia e Estatística, 2021. Cidades. IBGE, Rio de Janeiro. Available in: https://cidades.ibge.gov.br/. Accessed on: jan/2023.
KENDALL, M. A., & STUART, A. (1967). The advanced theory of statistics (2nd ed.). Londres, UK: Charles Griffin.
KENGN L, MBOUSSOP A N, KOPA A N, TANKOU C M, TEMATIO P, J.R. NDAM NGOUPAYOU N. (2019). Rainfall variability on the southern slope of the Bambouto mountain (West-Cameroon) and impact on the crop cultivation calendar. Journal of African Earth Sciences. Volume 154, June 2019, Pages 164-171. https://doi.org/10.1016/j.jafrearsci.2019.03.020
LIBONATI, R.; GEIRINHAS, J.O.L.; SILVA, P.S.; RUSSO, A.; RODRIGUES, J.A.; BELÉM, L.B.C.; NOGUEIRA, J.; ROQUE, F.O.; DA CAMARA, C.C.; NUNES, A.M.B.; MARENGO, J.A.; TRIGO, R.M. (2022). Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal. Environmental Research Letters, 17: 015005. https://doi.org/10.1088/1748-9326/ac462e
LIBONATI, R.; PEREIRA, J.M.C.; DA CAMARA, C.C.; PERES, L.F.; OOM, D.; RODRIGUES, J.A.; SANTOS, F.L.M.; TRIGO, R.M.; GOUVEIA, C.M.P.; MACHADO-SILVA, F.; ENRICH-PRAST, A.; SILVA, J.M.N. (2021). Twenty-first century droughts have not increasingly exacerbated fire season severity in the Brazilian Amazon. Scientific Reports, 11(1): 4400. https://doi.org/10.1038/s41598-021-82158-8
LORENÇONE, J.A.; APARECIDO, L.E.O.; LORENÇONE, P.A.; LIMA, R.F.; TORSONI, G.B. (2022). Assessment of climate change using humidity index of Thornthwaite climate classification in Pantanal biome. Revista Brasileira de Meteorologia,37(1):99-119. https://doi.org/10.1590/0102-7786370075
MANN, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245-259. doi: 0012-9682(194507)
MARENGO, J.A.; CUNHA, A.P.M.A.; NOBRE, C.A.; RIBEIRO NETO, G.G.; MAGALHAES, A.R.; TORRES, R.R.; SAMPAIO, G.; ALEXANDRE, F.; ALVES, L.M.; CUARTAS, L.A., DEUSDARÁ, K.R.L., ÁLVALA, R.C.S. (2020a). Assessing drought in the drylands of northeast Brazil under regional warming exceeding 4 °C. Natural Hazards, 103(2): 2589-2611. https://doi.org/10.1007/s11069-020-04097-3
MARENGO, J.A.; CAMARGO, C.C. (2008). Surface air temperature trends in Southern Brazil for 1960–2002. International Journal of Climatology, 28(7): 893-904. https://doi.org/10.1002/joc.1584
Marengo, J.A.; Bernasconi, M. 2015. Regional differences in aridity/drought conditions over Northeast Brazil: present state and future projections. Climatic Change, 129(1-2): 103-115. https://doi.org/10.1007/s10584-014-1310-1.
MARENGO, J.A.; CUNHA, A.P.; CUARTAS, L.A.; LEAL, K.R.D.; BROEDEL, E.; SELUCHI, M.E.; MICHELIN, C.M.; PRAGA BAIÃO, C.F.P.; ÂNGULO, E.C.; ALMEIDA, E.K.; KAZMIERCZAK, M.L.; MATEUS, N.P.A.; SILVA, R.C.; BENDER, F. (2021). Extreme drought in the brazilian Pantanal in 2019-2020: characterization, causes, and impacts. Frontiers in Water, 3: 639204. https://doi.org/10.3389/frwa.2021.639204
MARENGO, J.A.; JIMENEZ, J.C.; ESPINOZA, J.C.; CUNHA, A.P.; ARAGÃO, L.E.O. (2022). Increased climate pressure on the agricultural frontier in the eastern Amazonia – Cerrado transition zone. Scientific Reports, 12: 457. https://doi.org/10.1038/s41598-021-04241-4
MARENGO, J.A.; JONES, R.; ALVES, L.M.; VALVERDE, M.C. (2009). Future change of temperature and precipitation extremes in South America as derived from the PRECIS regional climate modeling system. International Journal of Climatology, 29(15): 2241-2255. https://doi.org/10.1002/joc.1863
MARENGO, J.A.; SOUZA, C.M.; THONICKE, K.; BURTON, C.; HALLADAY, K.; BETTS, R.A.; ALVES, L.M.; SOARES, W.R. (2018b). Changes in Climate and Land Use Over the Amazon Region: Current and Future Variability and Trends. Frontiers in Earth Science, 6: 228. https://doi.org/10.3389/feart.2018.00228
MÜLLER, G.V.; REPINALDO, C.R.R.; ARANEO, D.C. (2018). Extreme cold events in South America analyzed from a GFDL model perspective: comparison between CMIP3 and CMIP5 climate scenarios. Theoretical and Applied Climatology, 134(1-2): 453-466. https://doi.org/10.1007/s00704-017-2286-3
NOBRE, C.A.; SAMPAIO, G.; BORMA, L.S.; CASTILLA-RUBIO, J.C.; SILVA, J.S.; CARDOSO, M. (2016). Land-use and climate change risks in the amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences of the United States of America, 113(39): 10759-10768. https://doi.org/10.1073/pnas.1605516113
OLIVEIRA APARECIDO LE, ROLIM GDS, MORAES JRD, VALERIANO TTB, LENSE GHE (2018) Maturation periods for Coffea arabica cultivars and their implications for yield and quality in Brazil. J Sci Food Agric 98(10):3880–3891. https://doi.org/10.1002/jsfa. 8905
ORTEGA, G.; ARIAS, P.A.; VILLEGAS, J.C.; MARQUET, P.A., NOBRE, P. (2021). Present-day and future climate over central and South America according to CMIP5/CMIP6 models. International Journal of Climatology, 41(15): 6713-6735. https://doi.org/10.1002/joc.7221
PENEREIRO J C. 2020. Climatic trends of temperatures and precipitation in Brazilian localities. Acta Scientiarum. Technology, vol. 42, e44359.
PETTITT, A. N. (1979). A non-parametric approach to the change-point problem. Journal of the Royal Statistical Society, 28(2), 126-135. doi: 10.2307/2346729
Regoto P, Dereczynski C, Chou SC, Bazzanela AC (2021) Observed changes in air temperature and precipitation extremes over Brazil. International Journal of Climatology 41(11):5125-5142. https://doi.org/10.1002/joc.7119
REIS, C J ; SOUZA A ; GRAF, R ; KOSSOWSKI, T M. ; ABREU M C ; OLIVEIRA-JÚNIOR, J F ; FERNANDES, W A . (2022). Modeling of the air temperature using the Extreme Value Theory for selected biomes in Mato Grosso do Sul (Brazil). Stochastic Environmental Research And Risk Assessment, v. 22, p. 1.
SA'ADI, Z., SHAHID, S., ISMAIL, T., CHUNG, E. S., & WANG, X. J. (2019). Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann-Kendall test. Meteorology and Atmospheric Physics, 131(3), 263-277
SALVINO M F, GROPPO J D, PELLEGRINO G Q. (2016). Trends Analysis of Precipitation and Temperature Data in Brazil. Rev. bras. meteorol. 31 (1) • jan-mar 2016 • https://doi.org/10.1590/0102-778620150003
SILVA J S; JÓR CABRAL JÚNIOR J B; RODRIGUES D T; SILVA F D S. (2023). Climatology and significant trends in air temperature in Alagoas, Northeast Brazil. Theoretical and Applied Climatology (2023) 151:1805–1824. https://doi.org/10.1007/s00704-023-04358-8
SILVA, C.B.; SILVA, M.E.S.; AMBRIZZI, T.; TOMMASELLI, J.T.G.; PATUCCI, N.N.; MATAVELI, G.A.V.; LIMA, B.S.; CORREA, W.S. C. (2019). Precipitação na América do Sul – dados obtidos por estações meteorológicas automáticas e por sistemas orbitais. Revista Brasileira de Climatologia, 15(25): 54-79. https://doi.org/10.5380/abclima.v25i0.58813.
SILVEIRA, C.S.; FILHO, F.A.S.; COSTA, A.A.; CABRAL, S.L. (2013). Performance assessment of CMIP5 models concerning the representation of precipitation variation patterns in the twentieth century on the northeast of Brazil, Amazon and Prata Basin and analysis of projections or the scenery RCP8.5. Revista Brasileira de Meteorologia, 28(3): 317-330. https://doi.org/10.1590/S0102-77862013000300008
SOUZA A; ABREU M C ; OLIVEIRA-JÚNIOR, J F ; ARISTONE F ; FERNANDES, W A ; AVIV-SHARON, E ; GRAF, R. (2022) Climate Regionalization in Mato Grosso do Sul: A Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature. Brazilian Archives of Biology and Technology (Online), v. 65, p. 1-10.
SOUZA, A.; SANTOS, C. M; IHADDADENE, R.; CAVAZZANA, G.; ABREU, M. C. ; DE OLIVEIRA-JÚNIOR, J. F.; POBOCIKOVA, I.; DE GOIS, G.; LINS, T. M. P. (2021). Analysis of extreme monthly and annual air temperatures variability using regression model in Mato Grosso do Sul, Brazil. Modeling Earth Systems and Environment, v. 7, p. 1-17, 2021.
WARD, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244.
XAVIER, A.C.; KING, C.W.; SCANLON, B.R. (2016). Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology, 36(6): 2644-2659. https://doi.org/10.1002/joc.4518.
YUE, S., & WANG, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management, 18(3), 201-218. DOI: 10.1023/B: WARM.0000043140.61082.60
velopment, v. 194, 106980.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 GEOgraphia
This work is licensed under a Creative Commons Attribution 4.0 International License.
DECLARATION OF ORIGINALITY AND TRANSFER OF COPYRIGHT
I declare that this article is original, having not been submitted for publication in any other national or international journal, either in part or in its entirety. I further declare that, once published in the journal GEOgraphia, edited by the Graduate Program in Geography of the Universidade Federal Fluminense, it shall never be submitted by me or by any of the other co-authors to any other journal. I also declare that I am aware that failure to observe this commitment will subject the offender to the sanctions and penalties set forth in the Copyright Protection Law (No. 9609, of 02/19/1998).
The author grants and transfers, fully and free of charge, to the Graduate Program in Geography of the Universidade Federal Fluminense, on a permanent, irrevocable, and non-exclusive basis, all non-commercial patrimonial copyright relating to scientific articles published in the journal GEOgraphia. Signed texts are the sole responsibility of their authors and do not necessarily represent the opinion of the editors or members of the journal's Editorial Board.
Published works are simultaneously licensed under a Creative Commons Attribution 4.0 International License.
