Forecasting Climatic Trends Using Neural Networks: An Experimental Study Using Global Historical Data

Abstract

Climate change is undoubtedly one of the biggest problems in the 21st century. Currently, however, most research efforts on climate forecasting are based on mechanistic, bottom-up approaches such as physics-based general circulation models and earth system models. In this study, we explore the performance of a phenomenological, top-down model constructed using a neural network and big data of global mean monthly temperature. By generating graphical images using the monthly temperature data of 30 years, the neural network system successfully predicts the rise and fall of temperatures for the next 10 years. Using LeNet for the convolutional neural network, the accuracy of the best global model is found to be 97.0%; we found that if more training images are used, a higher accuracy can be attained. We also found that the color scheme of the graphical images affects the performance of the model. Moreover, the prediction accuracy differs among climatic zones and temporal ranges. This study illustrated that the performance of the top-down approach is notably high in comparison to the conventional bottom-up approach for decadal-scale forecasting. We suggest using artificial intelligence-based forecasting methods along with conventional physics-based models because these two approaches can work together in a complementary manner.

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

“…The ability of AI to process enormous amounts of non-structured, multi-dimensional data using sophisticated optimisation techniques is already facilitating the understanding of high-dimensional climate datasets and forecasting of future trends (Huntingford et al 2019 ). AI techniques have been used to forecast global mean temperature changes (Ise and Oba 2019 ; Cifuentes et al 2020 ); predict climactic and oceanic phenomena such as El Niño (Ham et al 2019 ), cloud systems (Rasp et al 2018 ), and tropical instability waves (Zheng et al 2020 ); better understand aspects of the weather system—like rainfall, generally (Sønderby et al 2020 ; Larraondo et al 2020 ) and in specific locales, such as Malaysia (Ridwan et al 2020 )—and their knock-on consequences, like water demand (Shrestha et al 2020 ; Xenochristou et al 2020 ). AI tools can also help anticipate the extreme weather events that are more common as a result of global climate change, for example heavy rain damage (Choi et al 2018 ) and wildfires (Jaafari et al 2019 ), and other downstream consequences, such as patterns of human migration (Robinson and Dilkina 2018 ).…”

The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations

“…The ability of AI to process enormous amounts of non-structured, multi-dimensional data using sophisticated optimisation techniques is already facilitating the understanding of high-dimensional climate datasets and forecasting of future trends (Huntingford et al 2019). AI techniques have been used to forecast global mean temperature changes (Ise and Oba 2019;Cifuentes et al 2020); predict climactic and oceanic phenomena such as El Niño (Ham, Kim, and Luo 2019), cloud systems (Rasp, Pritchard, and Gentine 2018), and tropical instability waves (Zheng et al 2020); better understand aspects of the weather system -like rainfall, generally (Sønderby et al 2020;Larraondo et al 2020) and in specific locales, such as Malaysia (Ridwan et al 2020) -and their knock-on consequences, like water demand (Shrestha, Manandhar, and Shrestha 2020;Xenochristou et al 2020).…”

Neural network models in reducing the risks of irrigated agriculture

“…Dynamic Artificial Neural Networks are the best use of resources for the agro-climatic forecasting problems and can reduce the environmental risk [17]. We propose to consider a distributed neural network with a time delay (TDNN) as a method for predicting the agroclimatic resources of a region.…”