IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p7801-d1143373.html
   My bibliography  Save this article

Short-Term Climate Prediction over China Mainland: An Attempt Using Machine Learning, Considering Natural and Anthropic Factors

Author

Listed:
  • Ruolin Li

    (Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
    Qilian Mountains Eco-Environment Research Center in Gansu Province, Lanzhou 730000, China
    Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Lanzhou 730000, China)

  • Celestin Sindikubwabo

    (Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Lanzhou 730000, China
    College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Qi Feng

    (Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
    Qilian Mountains Eco-Environment Research Center in Gansu Province, Lanzhou 730000, China
    Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Lanzhou 730000, China)

  • Yang Cui

    (Ningxia Key Laboratory for Meteorological Disaster Prevention and Reduction, Yinchuan 750002, China)

Abstract

Climate extremes pose significant natural threats to socioeconomic activities. Accurate prediction of short-term climate (STC) can provide relevant departments with warnings to effectively reduce this threat. To accurately predict STC in China, this study utilizes machine learning algorithms, particularly the random forest (RF) model, to evaluate the role of both natural and anthropogenic factors. Monthly temperature and precipitation data from 160 meteorological stations spanning China, as well as natural climate factors and an economic activity index, were obtained to perform a seasonal hindcast of air temperature and precipitation observed from 1979 to 2018. Our focus was to predict the seasonal mean temperature and precipitation, specifically the summer (June, July, and August (JJA)) and winter (December, January, and February (DJF)) air temperature and precipitation anomalies using forecast factors from the preceding season. Results show that a comprehensive consideration of both natural and anthropogenic effects provides a more accurate fit to the observed climate trends compared to using only one factor. When both factors were integrated, the model scores (coefficient of determination) exceeded 0.95, close to 1.00, which is significantly higher than those of natural (0.86 for temperature, 0.85 for precipitation) or anthropogenic (0.90 for temperature and 0.50 for precipitation) factors alone. Furthermore, we also attempted to predict similar components for 2019 and 2020. The average relative error between predictions and observations was less than 10%, indicating that this integrated model’s performance exhibited a significant improvement in predicting the STC. The findings of this study underscore the importance of accounting for both natural and anthropogenic factors in predicting climate trends to inform sustainable decision-making in China.

Suggested Citation

  • Ruolin Li & Celestin Sindikubwabo & Qi Feng & Yang Cui, 2023. "Short-Term Climate Prediction over China Mainland: An Attempt Using Machine Learning, Considering Natural and Anthropic Factors," Sustainability, MDPI, vol. 15(10), pages 1-18, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7801-:d:1143373
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/7801/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/10/7801/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. N. H. Saji & B. N. Goswami & P. N. Vinayachandran & T. Yamagata, 1999. "A dipole mode in the tropical Indian Ocean," Nature, Nature, vol. 401(6751), pages 360-363, September.
    2. Higgins, Nathaniel & Hintermann, Beat & Brown, Molly E., 2015. "A model of West African millet prices in rural markets," Food Policy, Elsevier, vol. 52(C), pages 33-43.
    3. Gerrit Hansen & Dáithí Stone, 2016. "Assessing the observed impact of anthropogenic climate change," Nature Climate Change, Nature, vol. 6(5), pages 532-537, May.
    4. Junqiang Wan & Honghai Zhang & Wenying Lyu & Jinlun Zhou, 2022. "A Novel Combined Model for Short-Term Emission Prediction of Airspace Flights Based on Machine Learning: A Case Study of China," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    5. Ariel Ortiz-Bobea & Toby R. Ault & Carlos M. Carrillo & Robert G. Chambers & David B. Lobell, 2021. "Anthropogenic climate change has slowed global agricultural productivity growth," Nature Climate Change, Nature, vol. 11(4), pages 306-312, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenju Cai & Yi Liu & Xiaopei Lin & Ziguang Li & Ying Zhang & David Newth, 2024. "Nonlinear country-heterogenous impact of the Indian Ocean Dipole on global economies," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Weiqing Han & Lei Zhang & Gerald A. Meehl & Shoichiro Kido & Tomoki Tozuka & Yuanlong Li & Michael J. McPhaden & Aixue Hu & Anny Cazenave & Nan Rosenbloom & Gary Strand & B. Jason West & Wen Xing, 2022. "Sea level extremes and compounding marine heatwaves in coastal Indonesia," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Zhang, Yang & Zhang, Yan & Gao, Yan & McLaughlin, Neil B. & Huang, Dandan & Wang, Yang & Chen, Xuewen & Zhang, Shixiu & Liang, Aizhen, 2024. "Effects of tillage practices on environment, energy, and economy of maize production in Northeast China," Agricultural Systems, Elsevier, vol. 215(C).
    4. Nisa Anil & M. R. Ramesh Kumar & R. Sajeev & P. K. Saji, 2016. "Role of distinct flavours of IOD events on Indian summer monsoon," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1317-1326, June.
    5. Liu, Yong & Ruiz-Menjivar, Jorge & Zhang, Junbiao, 2022. "Climate adaptation and technical efficiency of rice production in Central China," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322521, Agricultural and Applied Economics Association.
    6. Stefano Pinardi & Matteo Salis & Gabriele Sartor & Rosa Meo, 2023. "EU−Africa: Digital and Social Questions in a Multicultural Agroecological Transition for the Cocoa Production in Africa," Social Sciences, MDPI, vol. 12(7), pages 1-29, July.
    7. Akio Kitoh, 2007. "Variability of Indian monsoon-ENSO relationship in a 1000-year MRI-CGCM2.2 simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 42(2), pages 261-272, August.
    8. Iskhaq Iskandar & Deni Okta Lestari & Agus Dwi Saputra & Riza Yuliratno Setiawan & Anindya Wirasatriya & Raden Dwi Susanto & Wijaya Mardiansyah & Muhammad Irfan & Rozirwan & Joga Dharma Setiawan & Kun, 2022. "Extreme Positive Indian Ocean Dipole in 2019 and Its Impact on Indonesia," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    9. Yoshiyuki Kurachi & Hajime Morishima & Hiroshi Kawata & Ryo Shibata & Kazuma Bunya & Jin Moteki, 2022. "Challenges for Japan's Economy in the Decarbonization Process," Bank of Japan Research Papers 22-06-09, Bank of Japan.
    10. Anni Arumsari Fitriany & Piotr J. Flatau & Khoirunurrofik Khoirunurrofik & Nelly Florida Riama, 2021. "Assessment on the Use of Meteorological and Social Media Information for Forest Fire Detection and Prediction in Riau, Indonesia," Sustainability, MDPI, vol. 13(20), pages 1-13, October.
    11. R. S. Akhila & J. Kuttippurath & R. Rahul & A. Chakraborty, 2022. "Genesis and simultaneous occurrences of the super cyclone Kyarr and extremely severe cyclone Maha in the Arabian Sea in October 2019," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1133-1150, September.
    12. Khed, Vijayalaxmi D. & Jat, M. L. & Krishna, Vijesh V., 2022. "Incentives for Experimenting with Sustainable Intensification: Can Direct Payments to Farmers Help Diversify the Cropping Systems in South India?," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 0(Number 3), September.
    13. Yadav Prasad Joshi & Eun-Hye Kim & Jong-Hun Kim & Ho Kim & Hae-Kwan Cheong, 2016. "Associations between Meteorological Factors and Aseptic Meningitis in Six Metropolitan Provinces of the Republic of Korea," IJERPH, MDPI, vol. 13(12), pages 1-12, November.
    14. D. Chiru Naik & Sagar Rohidas Chavan & P. Sonali, 2023. "Incorporating the climate oscillations in the computation of meteorological drought over India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 2617-2646, July.
    15. Wang, Yuhan & Lewis, David J., 2024. "Wildfires and climate change have lowered the economic value of western U.S. forests by altering risk expectations," Journal of Environmental Economics and Management, Elsevier, vol. 123(C).
    16. Liu, Jin & Li, Rui & Li, Shuo & Meucci, Alberto & Young, Ian R., 2024. "Increasing wave power due to global climate change and intensification of Antarctic Oscillation," Applied Energy, Elsevier, vol. 358(C).
    17. Luis Guillermo Becerra-Valbuena, 2021. "Droughts and Agricultural Adaptation to Climate Change," Working Papers halshs-03420657, HAL.
    18. Khodran Alzahrani & Mubashar Ali & Muhammad Imran Azeem & Bader Alhafi Alotaibi, 2023. "Efficacy of Public Extension and Advisory Services for Sustainable Rice Production," Agriculture, MDPI, vol. 13(5), pages 1-17, May.
    19. Wencun Zhou & Zhengjia Liu & Sisi Wang, 2023. "Spatiotemporal Dynamics of the Cropland Area and Its Response to Increasing Regional Extreme Weather Events in the Farming-Pastoral Ecotone of Northern China during 1992–2020," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
    20. Kavya Johny & Maya L. Pai & S. Adarsh, 2022. "Investigating the multiscale teleconnections of Madden–Julian oscillation and monthly rainfall using time-dependent intrinsic cross-correlation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1795-1822, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7801-:d:1143373. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.