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Predominantly positive XCO2 anomalies in the Caatinga biome highlight carbon vulnerability
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  • Published: 08 February 2026

Predominantly positive XCO2 anomalies in the Caatinga biome highlight carbon vulnerability

  • Libério Junio Silva1,
  • Luis Miguel da Costa2,
  • Ricardo de Oliveira Bordonal3,
  • Alan Rodrigo Panosso2,
  • Thiago Torres Costa Pereira1,
  • Cassiano Gustavo Messias4 &
  • …
  • Newton La Scala Jr.2 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Climate sciences
  • Ecology
  • Environmental sciences

Abstract

The Caatinga biome, the only exclusively Brazilian biome, plays a crucial yet understudied role in regional and global carbon dynamics. Using column-averaged dry-air mole fraction of CO2 (XCO2) data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) between 2015 and 2022, this study investigates spatial and temporal anomalies across distinct phytoecological biozones of the Caatinga. Anomaly detection, spatial autocorrelation (Local Moran’s I), time-series modeling (ARIMA), and correlation analyses with vegetation and climate indices (NDVI, EVI, LAI, land surface temperature, and precipitation) were applied to evaluate the biome’s carbon balance. Results reveal heterogeneous XCO2 patterns, with predominantly negative or neutral anomalies, confirming the Caatinga’s role as a carbon sink, though punctuated by localized positive anomalies indicating emission hotspots. The Savanna-Steppe and Pioneer Formation biozones exhibited the strongest seasonal and spatial clustering of positive anomalies, highlighting vulnerability to land-use pressures and climatic extremes. Forested biozones, particularly Open and Dense Ombrophilous Forests, showed increasing anomaly trends in recent years, suggesting a potential weakening of sink capacity. Correlations revealed distinct biome-specific responses: positive associations between XCO2 and precipitation in transitional and pioneer formations, and negative associations with vegetation indices in savanna areas, emphasizing hydrological control of carbon fluxes. The findings demonstrate that the Caatinga exhibits both resilience and vulnerability, with its carbon balance strongly modulated by climatic variability, vegetation structure, and anthropogenic pressures. These results underscore the biome’s strategic role in climate mitigation and the urgent need for targeted conservation and restoration policies to safeguard its carbon sequestration potential.

Data availability

The datasets generated and/or analysed during the current study are available in the GitHub repository https://github.com/arpanosso/caatinga-xco2-carbon-vulnerability. The XCO2 data used in this study were obtained from the Orbiting Carbon Observatory-2 (OCO-2) dataset, available at https://ocov2.jpl.nasa.gov/science/oco-2-data-center/.

References

  1. Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148. https://doi.org/10.1038/nature02121 (2004).

    Google Scholar 

  2. Tam et al. Research on climate change in social psychology publications: A systematic review. Asian J. Soc. Psychol. 24(2), 117–143. https://doi.org/10.1111/AJSP.12477 (2021).

    Google Scholar 

  3. Mele, M. et al. Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources. Environ. Sci. Pollut. Res. 28(30), 41127–41134. https://doi.org/10.1007/S11356-021-13337-3 (2021).

    Google Scholar 

  4. Habibullah, M. S. et al. Impact of climate change on biodiversity loss: global evidence. Environ. Sci. Pollut. Res. 29(1), 1073–1086. https://doi.org/10.1007/S11356-021-15702-8 (2022).

    Google Scholar 

  5. Davis, S. J. et al. Future CO2 emissions and climate change from existing energy infrastructure. Science 329(5997), 1330–1333. https://doi.org/10.1126/SCIENCE.1188566 (2010).

    Google Scholar 

  6. Montzka, S. A. et al. Non-CO2 greenhouse gases and climate change. Nature 476, 43–50. https://doi.org/10.1038/nature10322 (2011).

    Google Scholar 

  7. Azevedo, T. R. et al. SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Sci. Data 5(1), 1–43. https://doi.org/10.1038/sdata.2018.45. (2018).

  8. Silva Junior, C. H. L. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaba2949. https://doi.org/10.1126/sciadv.aba2949 (2020).

    Google Scholar 

  9. Costa, G. B. et al. Seasonal ecosystem productivity in a seasonally dry tropical forest (Caatinga) using flux tower measurements and remote sensing data. Remote Sens. 14(16), 3955. https://doi.org/10.3390/rs14163955 (2022).

    Google Scholar 

  10. Ferreira, R. R. et al. An assessment of the MOD17A2 gross primary production product in the Caatinga biome, Brazil. Int. J. Remote Sens. 42(4), 1275–1291. https://doi.org/10.1080/01431161.2020.1826063 (2021).

    Google Scholar 

  11. de Oliveira, M. L. et al. Remote sensing-based assessment of land degradation and drought impacts over terrestrial ecosystems in Northeastern Brazil. Sci. Total Environ. 835, 155490. https://doi.org/10.1016/j.scitotenv.2022.155490 (2022).

    Google Scholar 

  12. Salami, G. et al. Biomass and carbon balance in a dry tropical forest area in Northeast Brazil. An. Acad. Bras. Cienc. 95(4), e20191250. https://doi.org/10.1590/0001-3765202320191250 (2023).

  13. Hakkarainen, J. et al. J. Direct space-based observations of anthropogenic CO2 emission areas from OCO-2. Geophys. Res. Lett. 43(21), 11400–11406. https://doi.org/10.1002/2016GL070885 (2016).

    Google Scholar 

  14. Araújo, S. et al. Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018. Environmental.

  15. Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393. https://doi.org/10.1038/s41586-021-03629-6 (2021).

    Google Scholar 

  16. Gloor, M. et al. The carbon balance of South America: a review of the status, decadal trends and main determinants. Biogeosciences 9, 5407–5430. https://doi.org/10.5194/bg-9-5407-2012 (2012).

    Google Scholar 

  17. Le Dumont, J. et al. Deep learning applied to CO2 power plant emissions quantification using simulated satellite images. Geosci. Model. Dev. 17, 1995–2014. https://doi.org/10.5194/gmd-17-1995-2024 (2024).

    Google Scholar 

  18. Biederman, J. A. et al. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America. Glob Change Biol. 23, 1372–1391. https://doi.org/10.1111/gcb.13686 (2017).

    Google Scholar 

  19. Jin, Z. et al. A global surface CO2 flux dataset (2015–2022) inferred from OCO-2 retrievals using the GONGGA inversion system. Earth Syst. Sci. Data. 16, 2857–2876. https://doi.org/10.5194/essd-16-2857-2024 (2024).

    Google Scholar 

  20. Crowell, S. et al. The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network. Atmos. Chem. Phys. 19, 9797–9831. https://doi.org/10.5194/acp-19-9797-2019 (2019).

    Google Scholar 

  21. Chevallier, F. et al. Large CO2 emitters as seen from satellite: comparison to a gridded global emission inventory. Geophys. Res. Lett. 49(5). e2021GL097540 (2022).

  22. Lian, J. et al. Analysis of temporal and spatial variability of atmospheric CO2 concentration within Paris from the GreenLITE™ laser imaging experiment. Atmos. Chem. Phys. 19, 13809–13825. https://doi.org/10.5194/acp-19-13809-2019 (2019).

    Google Scholar 

  23. Zheng, B. et al. Observing carbon dioxide emissions over China’s cities and industrial areas with the orbiting carbon Observatory-2. Atmos. Chem. Phys. 20, 8501–8510. https://doi.org/10.5194/acp-20-8501-2020 (2020).

    Google Scholar 

  24. Kuhlmann, G. et al. Quantifying CO2 emissions of a city with the copernicus anthropogenic CO2 monitoring satellite mission. Atmos. Meas. Tech. 13, 6733–6751. https://doi.org/10.5194/amt-13-6733-2020 (2020).

    Google Scholar 

  25. Zhu, Y. et al. The correlation between urban form and carbon emissions: A bibliometric and literature review. Sustainability 15(18), 13439. https://doi.org/10.3390/su151813439 (2023).

    Google Scholar 

  26. Chandra, N. et al. Estimated regional CO2 flux and uncertainty based on an ensemble of atmospheric CO2 inversions. Atmos. Chem. Phys. 22, 9215–9243. https://doi.org/10.5194/acp-22-9215-2022 (2022).

    Google Scholar 

  27. Worden, M. et al. Inferred drought-induced plant allocation shifts and their impact on drought legacy at a tropical forest site. Glob Change Biol. 30, e17287. https://doi.org/10.1111/gcb.17287 (2024).

    Google Scholar 

  28. Wunch, D. et al. Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON. Atmos. Meas. Tech. 10, 2209–2238. https://doi.org/10.5194/amt-10-2209-2017 (2017).

    Google Scholar 

  29. O’Dell, C. W. et al. Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 using the version 8 ACOS algorithm. Atmos. Meas. Tech. 11, 6539–6576. https://doi.org/10.5194/amt-11-6539-2018 (2018).

    Google Scholar 

  30. Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6 + improvements. Atmos. Meas. Tech. 7, 4353–4365. https://doi.org/10.5194/amt-7-4353-2014 (2014).

    Google Scholar 

  31. IBGE. Biomas E Sistema Costeiro-Marinho Do Brasil (Instituto Brasileiro de Geografia e Estatística, 2019).

  32. Silva, P. F. et al. Seasonal patterns of carbon dioxide, water and energy fluxes over the Caatinga and grassland in the semi-arid region of Brazil. J. Arid Environ. 147, 71–82. https://doi.org/10.1016/j.jaridenv.2017.09.003 (2017).

    Google Scholar 

  33. Rodrigues, J. A. et al. Spatial-temporal dynamics of Caatinga vegetation cover by remote sensing in the Brazilian semiarid region. Dyna 87(215), 109–117. https://doi.org/10.15446/dyna.v87n215.8785 (2020).

    Google Scholar 

  34. Gomes, L. et al. Impacts of fire frequency on net CO2 emissions in the Cerrado savanna vegetation. Fire 7(8), 280. https://doi.org/10.3390/fire7080280 (2024).

    Google Scholar 

  35. Moro, M. F. et al. A phytogeographical metaanalysis of the semiarid Caatinga domain in Brazil. Bot. Rev. 82, 91–148. https://doi.org/10.1007/s12229-016-9164-z (2016).

    Google Scholar 

  36. O’Dell, C. W. et al. Improved retrievals of carbon dioxide from OCO-2. Atmos. Meas. Tech. 11, 6539–6574. https://doi.org/10.5194/amt-11-6539-2018 (2018).

    Google Scholar 

  37. O’Dell, C. W. et al. The ACOS CO2 retrieval algorithm – Part 1: description and validation. Atmos. Meas. Tech. 5, 99–121. https://doi.org/10.5194/amt-5-99-2012 (2012).

    Google Scholar 

  38. Kiel, M. et al. How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates. Atmos. Meas. Tech. 12, 2241–2259. https://doi.org/10.5194/amt-12-2241-2019 (2019).

    Google Scholar 

  39. Crisp, D. et al. The OCO-2 instrument: calibration and performance. Atmos. Meas. Tech. 10, 59–81. https://doi.org/10.5194/amt-10-59-2017 (2017).

    Google Scholar 

  40. Kataoka, F. et al. The cross-calibration of spectral radiances and cross-validation of CO2 estimates from GOSAT and OCO-2. Remote Sens. 9, 1158. https://doi.org/10.3390/rs9111158 (2017).

    Google Scholar 

  41. NASA/GSFC. OCO-2 Level 2 bias-corrected Lite Files, Version 10r. Goddard Earth Sciences Data and Information Services Center (GES DISC). https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary (2023).

  42. Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Google Scholar 

  43. Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2 (2002).

    Google Scholar 

  44. Schulz, K. et al. Grazing deteriorates the soil carbon stocks of Caatinga forest ecosystems in Brazil. For. Ecol. Manag. 367, 62–70. https://doi.org/10.1016/j.foreco.2016.02.011 (2016).

    Google Scholar 

  45. Wan, Z. New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sens. Environ. 140, 36–45. https://doi.org/10.1016/j.rse.2013.08.027 (2014).

    Google Scholar 

  46. Abatzoglou, J. T. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data. 5, 170191. https://doi.org/10.1038/sdata.2017.191 (2018).

  47. R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2025).

  48. Costa, G. B. et al. Seasonal ecosystem productivity in a seasonally dry tropical forest (Caatinga) using flux tower measurements and remote sensing data. Remote Sens. 14, 3955. https://doi.org/10.3390/rs14163955 (2022).

    Google Scholar 

  49. Mendes, K. R. et al. Interannual variability of energy and CO2 exchanges in a remnant area of the Caatinga biome under extreme rainfall conditions. Sustainability 15(13), 10085. https://doi.org/10.3390/su151310085 (2023).

    Google Scholar 

  50. Botía, S. et al. Combined CO2 measurement record indicates Amazon forest carbon uptake is offset by savanna carbon release. Atmos. Chem. Phys. 25, 6219–6255 (2025).

    Google Scholar 

  51. Mendes, K. R. et al. Seasonal variation in net ecosystem CO2 exchange of a Brazilian seasonally dry tropical forest. Sci. Rep. 10, 9454. https://doi.org/10.1038/s41598-020-66415-w (2020).

    Google Scholar 

  52. da Costa, L. M., Davitt, A., Volpato, G., Costa de Mendonça, G., Panosso, A. R. & La Scala Jr., N. A comparative analysis of GHG inventories and ecosystems carbon absorption in Brazil. Sci. Total Environ. 958, 177932. https://doi.org/10.1016/j.scitotenv.2024.177932 (2025).

  53. Mendes, K. R. et al. The Caatinga dry tropical forest: a highly efficient carbon sink in South America. Agric. Meteorol. 369, 110573. https://doi.org/10.1016/j.agrformet.2025.110573 (2025).

    Google Scholar 

  54. Silva LFdS, Pessoa, L. G. M. et al. Changes in soil C, N, and P concentrations and stocks after Caatinga natural regeneration of degraded pasture areas in the Brazilian semiarid region. Sustainability 16(20), 8737. https://doi.org/10.3390/su16208737 (2024).

    Google Scholar 

  55. Freitas ICd, Alves, M. A. et al. Soil carbon and nitrogen stocks under agrosilvopastoral systems with different arrangements in a transition area between Cerrado and Caatinga biomes in Brazil. Agronomy 12(12), 2926. https://doi.org/10.3390/agronomy12122926 (2022).

    Google Scholar 

  56. Viana-Lima, A. Y. et al. From overgrazed land to forests: assessing soil health in the Caatinga biome. J. Environ. Manage. 374, 124022. https://doi.org/10.1016/j.jenvman.2024.124022 (2025).

    Google Scholar 

  57. Rocha, W. et al. Drought and fire affect soil CO2 efflux and use of non-structural carbon by roots in forests of Southern Amazonia. For. Ecol. Manage. 585, 122584. https://doi.org/10.1016/j.foreco.2025.122584 (2025).

    Google Scholar 

  58. Medeiros, R. et al. Remote sensing phenology of the Brazilian Caatinga and its environmental drivers. Remote Sens. 14(11), 2637. https://doi.org/10.3390/rs14112637 (2022).

    Google Scholar 

  59. GONGGA Model Intercomparison Project. Global Carbon Sink Variability Report (Chinese Academy of Sciences, 2025).

  60. Butz, A. et al. Toward accurate CO2 and CH4 observations from GOSAT. Geophys. Res. Lett. 38, L14802. https://doi.org/10.1029/2011GL047393 (2011).

    Google Scholar 

  61. Mendes, K. R. et al. The Caatinga dry tropical forest: a highly efficient carbon sink in South America. Agric. For. Meteorol. 369, 110573. https://doi.org/10.1016/j.agrformet.2025.110573 (2025).

    Google Scholar 

  62. Taylor, T. E. et al. Orbiting carbon observatory (OCO-2) instrument performance and calibration. Atmos. Meas. Tech. 13, 123–140. https://doi.org/10.5194/amt-13-123-2020 (2020).

    Google Scholar 

  63. Helmholtz Centre for Environmental Research-UFZ, et al et al. The long-term consequences of forest fires on the carbon fluxes of a tropical forest. Appl. Sci. 11(10), 4696. https://doi.org/10.3390/app11104696 (2021).

    Google Scholar 

  64. Niu, Y. et al. Variations in diurnal and seasonal net ecosystem carbon dioxide exchange in a semiarid sandy grassland ecosystem in China’s Horqin sandy land. Biogeosciences 17, 6309–6324. https://doi.org/10.5194/bg-17-6309-2020 (2020).

    Google Scholar 

  65. Ramos, D. et al. Front. Environ. Sci. 11:1275844. doi:https://doi.org/10.3389/fenvs.2023.1275844 (2023).

    Google Scholar 

  66. de Araujo, M. D. et al. Seasonal ecosystem productivity in a seasonally dry tropical forest (Caatinga) using flux tower measurements and remote sensing data. Remote Sens. 14(16), 3955. https://doi.org/10.3390/rs14163955 (2022).

    Google Scholar 

  67. Fischer, R. et al. The Long-term consequences of forest fires on the carbon fluxes of a tropical forest in Africa. Appl. Sci. 11(10), 4696. https://doi.org/10.3390/app11104696 (2021).

    Google Scholar 

  68. Silva, T. S. F. et al. Vegetation structure and phenology in Brazilian dry forests using remote sensing. Biotropica 49, 640–651. https://doi.org/10.1111/btp.12415 (2017).

    Google Scholar 

  69. Medeiros, R. et al. Remote sensing phenology of the Brazilian Caatinga and its environmental drivers. Remote Sens. 14, 2637. https://doi.org/10.3390/rs14112637 (2022).

    Google Scholar 

  70. Zou, L. et al. Assessing the temporal response of tropical dry forests to droughts using remote sensing. Remote Sens. 12, 2341. https://doi.org/10.3390/rs12142341 (2020).

    Google Scholar 

  71. Barbosa, H. A., Kumar, T. V. L., Paredes, F., Elliott, S. & Ayuga, J. G. Assessment of Caatinga response to drought using Meteosat-SEVIRI normalized difference vegetation index (2008–2016). ISPRS J. Photogramm Remote Sens. 148, 235–252. https://doi.org/10.1016/j.isprsjprs.2018.12.014 (2019).

    Google Scholar 

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Acknowledgments

The authors acknowledge the Universidade do Estado de Minas Gerais (UEMG) for covering the Article Processing Charge (APC) associated with the publication of this article.

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Authors and Affiliations

  1. Graduate Program in Environmental Sciences, State University of Minas Gerais, Av. Professor Mário Palmério, 1001–Universitário District, Frutal, Brazil

    Libério Junio Silva & Thiago Torres Costa Pereira

  2. Department of Engineering and Exact Sciences, São Paulo State University (FCAV–UNESP), Via de Acesso Prof. Paulo Donato Castellane S/n, Jaboticabal, 14884-900, Brazil

    Luis Miguel da Costa, Alan Rodrigo Panosso & Newton La Scala Jr.

  3. Brazilian Biorenewables National Laboratory/Brazilian Center for Research in Energy and Materials (LNBR/CNPEM), Campinas, São Paulo, Brazil

    Ricardo de Oliveira Bordonal

  4. General Coordination for Land Sciences (CGCT), National Institute for Space Research (INPE), Av. dos Astronautas, 1758, Jardim da Granja, São José dos Campos, SP, 12227-010, Brazil

    Cassiano Gustavo Messias

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Contributions

L.J.S., L.M.C., R.B., A.R.P., T.T.C.P., C.G.M., and N.L.S.J. contributed equally to the writing of the manuscript, preparation of the figures, and revision of the text. All authors reviewed and approved the final version of the manuscript.

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Silva, L.J., da Costa, L.M., de Oliveira Bordonal, R. et al. Predominantly positive XCO2 anomalies in the Caatinga biome highlight carbon vulnerability. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37629-1

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  • Received: 29 September 2025

  • Accepted: 23 January 2026

  • Published: 08 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-37629-1

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Keywords

  • Caatinga biome
  • Carbon cycle
  • OCO-2 satellite
  • XCO2 anomalies
  • Climate variability.
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