From Data to Policy : Strengthening Essential Climate Variable Monitoring with Deep Learning Algorithms and Data Quality Standards

Jean-Philippe Montillet, Gaël Kermarrec, Ehsan Forootan, C. K. Shum, Shaoxing Mo, Wolfgang Finsterle, Karolin Samsel

Essential Climate Variables (ECVs) are critical for
understanding and monitoring climate systems, providing important
data to assess climate change and supporting policy
formulation. This review emphasizes the importance of ensuring
data quality, traceability, and consistency to derive reliable
features from ECV datasets, addressing challenges such as
temporal and spatial coverage gaps, calibration discrepancies,
and harmonization across diverse sources. Furthermore, we
highlight both the transformative potential and limits of advanced
analytics, including artificial intelligence (AI), in enhancing the
monitoring and prediction of ECVs using three case studies:
(i) climate modeling and prediction of temperatures for planning
scenario with machine learning, (ii) the Earth’s surface processes,
and (iii) monitoring the Earth radiation budget. This article also explores how ECVs are integrated into global frameworks like the Global Climate Observing System (GCOS) and the WMO Integrated Global Observing System (WIGOS), which establish standardized protocols for reliable and interoperable data. By
synthesizing advances in technology, data quality practices, and global collaboration efforts, this review underscores the importance
of interdisciplinary approaches to bridge the gap between
scientific knowledge and actionable climate policies.