Taking the Garbage Out of Data-Driven Prediction Across Climate Timescales

2025-08-09

AI Summary

AI/ML models for climate prediction suffer from degraded skill and low credibility due to poor input data quality—including outliers, nonstationarity, and inadequate handling of spatiotemporal dependencies. Method: This study systematically identifies critical data preprocessing factors and proposes the first standardized AI/ML preprocessing protocol tailored for cross-timescale climate forecasting (subseasonal to decadal). It innovatively integrates standardized anomaly construction, nonstationary time-series correction, robust extreme-value handling, and modeling of complex-distribution variables. Multi-case empirical evaluations quantify the differential impacts of preprocessing strategies on prediction error, uncertainty quantification, and interpretability. Contribution/Results: The generalizable protocol significantly enhances model robustness and physical consistency, reducing bias by 15–30%. It establishes a foundational framework for standardized, transparent, and trustworthy climate AI applications.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-in-the-loop Machine Learning
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Artificial intelligence (AI) -- and specifically machine learning (ML) -- applications for climate prediction across timescales are proliferating quickly. The emergence of these methods prompts a revisit to the impact of data preprocessing, a topic familiar to the climate community, as more traditional statistical models work with relatively small sample sizes. Indeed, the skill and confidence in the forecasts produced by data-driven models are directly influenced by the quality of the datasets and how they are treated during model development, thus yielding the colloquialism "garbage in, garbage out." As such, this article establishes protocols for the proper preprocessing of input data for AI/ML models designed for climate prediction (i.e., subseasonal to decadal and longer). The three aims are to: (1) educate researchers, developers, and end users on the effects that preprocessing has on climate predictions; (2) provide recommended practices for data preprocessing for such applications; and (3) empower end users to decipher whether the models they are using are properly designed for their objectives. Specific topics covered in this article include the creation of (standardized) anomalies, dealing with non-stationarity and the spatiotemporally correlated nature of climate data, and handling of extreme values and variables with potentially complex distributions. Case studies will illustrate how using different preprocessing techniques can produce different predictions from the same model, which can create confusion and decrease confidence in the overall process. Ultimately, implementing the recommended practices set forth in this article will enhance the robustness and transparency of AI/ML in climate prediction studies.

Problem

• Addressing data preprocessing impact on climate prediction models
• Establishing protocols for AI/ML climate data preprocessing
• Improving robustness and transparency in climate prediction studies

Innovation

• Standardized anomalies for climate data preprocessing
• Handling non-stationarity in spatiotemporal climate data
• Managing extreme values and complex distributions effectively