Version 1
: Received: 5 January 2024 / Approved: 8 January 2024 / Online: 8 January 2024 (09:19:12 CET)
How to cite:
Olasehinde, D.A.; Adeniran, K.A.; Ogunrinde, A.T.T. Integrating Multimodal Ensemble and Spatiotemporal Analysis for Enhanced Climate Modelling in Nigeria. Preprints2024, 2024010607. https://doi.org/10.20944/preprints202401.0607.v1
Olasehinde, D.A.; Adeniran, K.A.; Ogunrinde, A.T.T. Integrating Multimodal Ensemble and Spatiotemporal Analysis for Enhanced Climate Modelling in Nigeria. Preprints 2024, 2024010607. https://doi.org/10.20944/preprints202401.0607.v1
Olasehinde, D.A.; Adeniran, K.A.; Ogunrinde, A.T.T. Integrating Multimodal Ensemble and Spatiotemporal Analysis for Enhanced Climate Modelling in Nigeria. Preprints2024, 2024010607. https://doi.org/10.20944/preprints202401.0607.v1
APA Style
Olasehinde, D.A., Adeniran, K.A., & Ogunrinde, A.T.T. (2024). Integrating Multimodal Ensemble and Spatiotemporal Analysis for Enhanced Climate Modelling in Nigeria. Preprints. https://doi.org/10.20944/preprints202401.0607.v1
Chicago/Turabian Style
Olasehinde, D.A., Kamoru A Adeniran and Akinwale T. T. Ogunrinde. 2024 "Integrating Multimodal Ensemble and Spatiotemporal Analysis for Enhanced Climate Modelling in Nigeria" Preprints. https://doi.org/10.20944/preprints202401.0607.v1
Abstract
The study combines two critical aspects of climate research in Nigeria. The first segment focuses on improving predictive performance of global circulation models (GCMs) employing ensemble techniques. ANN ensemble displayed significant improvement in the accuracy of climate projections, particularly in high-altitude geographic areas where traditional models exhibit limitations. Performance evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Nash–Sutcliffe Efficiency (NSE) demonstrate the effectiveness of the ANN multimodal ensemble. Building on the improved climate modelling, the second segment delves into a spatial and temporal analysis of rainfall and mean temperature patterns under climate change in Nigeria. By considering two Representative Concentration Pathways (RCPs), namely RCP4.5 and RCP8.5, the study utilizes dynamically downscaled GCMs obtained from the Coupled Model Intercomparison Project phase 5 (CMIP5). The Mann-Kendall test reveals significant spatiotemporal variations in climate trends across the country. The warming trend is particularly pronounced in the northern regions, with RCP8.5 showing more severe impacts compared to RCP4.5. This integrated approach not only highlights the importance of multimodal ensembles in climate modelling but also provides valuable insights into the spatial and temporal patterns of climate change to inform effective adaptation and mitigation strategies for Nigeria.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
