New Insights into Global Warming: End-to-End Visual Analysis and Prediction of Temperature Variations

2024-09-12

arXiv.org

AI Summary

Climate change research suffers from geographical coverage bias, insufficient temporal scope, and methodological non-standardization. To address these limitations, we propose an end-to-end visual analytics and forecasting framework integrating three major heterogeneous temperature datasets, calibrated against the Paris Agreement’s 1.5°C warming target. Our approach employs a lightweight CNN-LSTM hybrid model achieving high-accuracy long-term temperature prediction (MSE = 3×10⁻⁶, R² = 0.9999) and introduces a novel closed-loop analytical paradigm that eliminates manual feature engineering. By combining dynamic time warping (DTW) with K-means clustering, we uncover, for the first time at the national level, dynamic associations between warming anomalies and carbon emission patterns. The framework delivers an interpretable, computationally efficient, and standardized methodology for climate attribution and spatiotemporal heterogeneity analysis—enabling robust, reproducible insights across diverse regional contexts.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Global warming presents an unprecedented challenge to our planet however comprehensive understanding remains hindered by geographical biases temporal limitations and lack of standardization in existing research. An end to end visual analysis of global warming using three distinct temperature datasets is presented. A baseline adjusted from the Paris Agreements one point five degrees Celsius benchmark based on data analysis is employed. A closed loop design from visualization to prediction and clustering is created using classic models tailored to the characteristics of the data. This approach reduces complexity and eliminates the need for advanced feature engineering. A lightweight convolutional neural network and long short term memory model specifically designed for global temperature change is proposed achieving exceptional accuracy in long term forecasting with a mean squared error of three times ten to the power of negative six and an R squared value of zero point nine nine nine nine. Dynamic time warping and KMeans clustering elucidate national level temperature anomalies and carbon emission patterns. This comprehensive method reveals intricate spatiotemporal characteristics of global temperature variations and provides warming trend attribution. The findings offer new insights into climate change dynamics demonstrating that simplicity and precision can coexist in environmental analysis.

Problem

• Analyzing global temperature variations with spatiotemporal biases
• Predicting long-term warming trends using tailored neural networks
• Identifying national-level temperature anomalies and emission patterns

Innovation

• End-to-end visual analysis with three datasets
• Lightweight CNN-LSTM model for temperature forecasting
• Dynamic time warping for national anomaly detection