SavedAdd your journal

Journals / Advances in Artificial Intelligence Research / 2025 / Cilt: 5 Sayı: 2

Temporal and Spatial Analysis of the Relationship Between Atmospheric CO₂ Concentration and Precipitation in Türkiye Based on Satellite Data

Atmosferik CO₂ Konsantrasyonu ile Yağış Arasındaki İlişkinin Uydu Verilerine Dayalı Olarak Türkiye Üzerinde Zamansal ve Mekânsal Analizi

İnayet Karadaş(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0001-8809-9110
Tuncay Yiğit(SULEYMAN DEMIREL UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING)0000-0001-7397-7224
İsmail Serkan Üncü(ISPARTA UNIVERSITY OF APPLIED SCIENCES)0000-0003-4345-761X
Mevlüt Ersoy(SULEYMAN DEMIREL UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING)0000-0003-2963-7729
Pages
42–55
Publication Date
2025-12-23
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results show that changes in precipitation cannot be explained by CO₂ alone.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The satellite-based carbon dioxide data is used in discovering any relationship between CO₂ and precipitation over Türkiye.
📊
Key Findings & Data:The CO₂ data have been obtained from NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission, while precipitation data were taken from Integrated Multi-satellite Retrievals for GPM (IMERG).
💡
Conclusion & Impact:These results show that changes in precipitation cannot be explained by CO₂ alone.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:CO₂ konsantrasyonu · yağış analizi · OCO-2

Abstract

The satellite-based carbon dioxide data is used in discovering any relationship between CO₂ and precipitation over Türkiye. The CO₂ data have been obtained from NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission, while precipitation data were taken from Integrated Multi-satellite Retrievals for GPM (IMERG). Both datasets have first been brought to the same time intervals; therefore, both temporal and spatial analyses could be conducted. There is a very weak positive correlation between CO₂ and rainfall (r ≈ 0.17). This becomes strongest with a 12-month lag as determined by Linear regression, which also explains only a very small proportion of the variability (R² ≈ 0.03). These results show that changes in precipitation cannot be explained by CO₂ alone. The novelties are temporally in using satellite datasets which cover the same period for Türkiye and applying machine learning models as well as classical statistical methods. Random Forest was the best performer among the tested models (R² = 0.68), suggesting that non-linear methods can capture patterns that simple linear regression misses; however, this result should be interpreted with caution given the short time series and limited set of predictors. This research contains a new local version of an updated CO₂–precipitation relationship with an explanation of its weak role and strongly emphasizes that precipitation cannot be explained by CO2.

Özet

Tablo verilerine dayalı karbon dioksit (CO₂) verileri, Türkiye üzerinde CO₂ ile yağış arasındaki ilişkiyi keşfetmek için kullanılmaktadır. CO₂ verileri, NASA'nın Yörüngedeki Karbon Gözlem Aracı-2 (OCO-2) misyonundan alınırken, yağış verileri, GPM için Entegre Çoklu Uydu Alımları (IMERG) veri setinden alınmıştır. Her iki veri seti de aynı zaman aralıklarına getirilmiş olup, bu sayede hem zamansal hem de mekansal analizler yapılabilmiştir. CO₂ ile yağış arasında çok zayıf bir pozitif korelasyon (r ≈ 0.17) bulunmuştur. Bu ilişki, 12 aylık bir gecikme ile en güçlü hale gelmektedir; doğrusal regresyon ile yapılan analizde ise bu ilişki yalnızca çok küçük bir değişkenlik oranını açıklamaktadır (R² ≈ 0.03). Bu sonuçlar, yağışlardaki değişimlerin yalnızca CO₂ ile açıklanamayacağını göstermektedir. Yenilikler, Türkiye için aynı dönemi kapsayan uydu verilerinin zamansal olarak kullanılması ve makine öğrenmesi modellerinin yanı sıra klasik istatistiksel yöntemlerin uygulanmasıdır. Test edilen modeller arasında Random Forest en iyi performansı sergileyerek (R² = 0.68), doğrusal regresyonun kaçırdığı desenleri yakalayabilen doğrusal olmayan yöntemlerin etkili olduğunu göstermektedir. Ancak bu sonuç, kısa zaman serisi ve sınırlı tahminci seti göz önünde bulundurularak dikkatlice yorumlanmalıdır. Bu araştırma, CO₂–yağış ilişkisine dair yeni bir yerel versiyon sunmakta ve CO₂'nin bu ilişkideki zayıf rolünü açıklamakta, aynı zamanda yağışların CO₂ ile açıklanamayacağını vurgulamaktadır.

🔖

Cite This Article

Karadaş, İ., Yiğit, T., Üncü, İ. S., Ersoy, M. (2025). Temporal and Spatial Analysis of the Relationship Between Atmospheric CO₂ Concentration and Precipitation in Türkiye Based on Satellite Data. Advances in Artificial Intelligence Research, Cilt: 5 Sayı: 2, 42–55. https://doi.org/10.54569/aair.1756108

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (28)

  1. Trenberth, K. E., Fasullo, J. T., & Kiehl, J. (2011). “Earth’s global energy budget”, Bulletin of the American Meteorological Society, 90(3), 311–323. https://doi.org/10.1175/2008BAMS2634.1
  2. IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/
  3. Wunch, D., Toon, G. C., Blavier, J. F. L., et al. (2017). "Validation of the OCO-2 XCO₂ measurements against TCCON data using the TCCON network.", Atmospheric Measurement Techniques, 10, 2209–2238. https://doi.org/10.5194/amt-10-2209-2017
  4. Friedlingstein, P., Jones, M. W., O’Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J.,. & Peters, G. P. (2022). Global Carbon Budget 2022. Earth System Science Data, 14(11), 4811–4900. https://doi.org/10.5194/essd-14-4811-2022
  5. Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J.,. Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
  6. Elliott, W. P., Angell, J. K., & Thoning, K. W. (1991). Relation of atmospheric CO₂ to tropical sea and air temperatures and precipitation. Tellus B: Chemical and Physical Meteorology, 43(2), 144–155. https://doi.org/10.3402/tellusb.v43i2.15259
  7. Yang, X., & Wang, M. (2000). Monsoon ecosystems control on atmospheric CO₂ interannual variability: Inferred from a significant positive correlation between year-to-year changes in land precipitation and atmospheric CO₂ growth rate. Geophysical Research Letters, 27(11), 1671–1674. https://doi.org/10.1029/1999GL006073
  8. Zhou, X., Weng, E., & Luo, Y. (2008). Modeling patterns of nonlinearity in ecosystem responses to temperature, CO₂, and precipitation. Ecological Applications, 18(3), 453–466. https://doi.org/10.1890/07-0626.1
  9. Betts, R. A., Boucher, O., Collins, M., Cox, P. M., Falloon, P. D., Gedney, N.,. & Woodward, F. I. (2007). Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature, 448(7157), 1037–1041. https://doi.org/10.1038/nature06045
  10. Eldering, A., Wennberg, P. O., Crisp, D., Schimel, D. S., Gunson, M. R., Chatterjee, A., Liu, J., Schwandner, F. M., Sun, Y., O’Dell, C. W., Frankenberg, C., Taylor, T., Fisher, B., Osterman, G. B., Wunch, D., Hakkarainen, J., Tamminen, J., & Weir, B. (2017). The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes. Science, 358(6360), eaam5745. https://doi.org/10.1126/science.aam5745
  11. Mousavi, S. M., Dinan, N. M., Ansarifard, S., & Sonnentag, O. (2022). Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020. Atmospheric Environment: X, 14, 100163. https://doi.org/10.1016/j.aeaoa.2022.100163
  12. Huffman GJ, Bolvin DT, Braithwaite D, Hsu K, Joyce R, Xie P, et al. "Integrated Multi satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG)", in Satellite Precipitation Measurement (Advances in Global Change Research, vol. 67), Springer, 2020, pp. 343–353. https://doi.org/10.1007/978-3-030-24568-9_19
  13. NASA. (2015). OCO-2 Science Team: OCO-2 Data User’s Guide. Jet Propulsion Laboratory, California Institute of Technology.
  14. Tiwari, Y. K., Revadekar, J. V., & Ravi Kumar, K. (2013). Variations in atmospheric Carbon Dioxide and its association with rainfall and vegetation over India. Atmospheric Environment, 68, 45-51. https://doi.org/10.1016/j.atmosenv.2012.11.040
  15. Leuzinger, S., Körner, C., Asshoff, R., & Gehrig, R. (2009). Rainfall distribution is the main driver of runoff under future CO₂. Global Change Biology, 15(8), 2124–2138. https://doi.org/10.1111/j.1365-2486.2009.01937.x
  16. Sponseller, R. A., Virginia, R. A., & Schimel, J. P. (2006). Precipitation pulses and soil CO₂ flux in a Sonoran Desert ecosystem. Global Change Biology, 12(4), 653–665. https://doi.org/10.1111/j.1365-2486.2006.01307.x
  17. Türkeş, M., & Erlat, E. (2003). Precipitation changes and variability in Türkiye linked to the North Atlantic Oscillation during the period 1930–2000. International Journal of Climatology, 23(14), 1771–1796. https://doi.org/10.1002/joc.962
  18. Ersoy, A., Uğuz, H., & Yurdusev, M. A. (2023). Rainfall forecasting with hybrid and machine learning models based on hyperparameter optimization. Journal of Hydrometeorology, 24(5), 755–770. https://doi.org/10.1061/JHYEFF.HEENG-5960
  19. Crane, R. G., Hewitson, B. C., & du Plessis, J. A. (1998). Doubled CO₂ precipitation changes for the Susquehanna Basin: Down-scaling from the GENESIS general circulation model. International Journal of Climatology, 18(1), 65–76. https://doi.org/10.1002/(SICI)1097-0088(199801)18:1 3.0.CO;2-9
  20. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
  21. Keenan, T. F., Williams, C. A., Gray, J., Lindenmaier, R., Michaelis, A. R., Campbell, J. E.,. & Wang, H. (2016). Recent pause in the growth rate of atmospheric CO₂ due to enhanced terrestrial carbon uptake. Nature Communications, 7, 13428. https://doi.org/10.1038/ncomms13428
  22. Fischer, E. M., & Knutti, R. (2015). Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560–564. https://doi.org/10.1038/nclimate2617
  23. Jet Propulsion Laboratory (JPL). (2015). Orbiting Carbon Observatory-2 (OCO-2) Data Product User’s Guide: Operational L1 and L2 Data Versions 6. California Institute of Technology. Retrieved from https://ocov2.jpl.nasa.gov/
  24. NASA Precipitation Processing System (PPS). (2023). Integrated Multi-satellite Retrievals for GPM (IMERG) [Data set]. NASA. Retrieved from https://gpm.nasa.gov/data/imerg
  25. Friedlingstein, P., Jones, M. W., O’Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J.,. & Peters, G. P. (2022). Global Carbon Budget 2022. Earth System Science Data, 14(11), 4811–4900. https://doi.org/10.5194/essd-14-4811-2022
  26. Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J.,. Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
  27. Betts, R. A., Boucher, O., Collins, M., Cox, P. M., Falloon, P. D., Gedney, N.,. & Woodward, F. I. (2007). Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature, 448(7157), 1037–1041. https://doi.org/10.1038/nature06045
  28. Keenan, T. F., Williams, C. A., Gray, J., Lindenmaier, R., Michaelis, A. R., Campbell, J. E.,. & Wang, H. (2016). Recent pause in the growth rate of atmospheric CO₂ due to enhanced terrestrial carbon uptake. Nature Communications, 7, 13428. https://doi.org/10.1038/ncomms13428