Decoding climate anomalies—From scientifically sound to societally relevant
- 1Department of Geographical Sciences, University of Maryland, College Park, MD 20742
- 2European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy
- 3Global Water Practice, The World Bank, Washington, DC 20433
Abstract
As climate impacts worsen, decision-makers, researchers, and public sector institutions increasingly need reliable, relevant, and appropriate information about emerging weather and climate anomalies. However, with the ever-expanding choice of datasets based on ground observations, satellite data, and models, it is critical to not only use the best dataset for a specific decision-making need but to avoid misinforming and even derailing decision-making due to dataset selection. The main objective of this perspective is to illustrate the importance of understanding datasets used to translate climate data into actionable knowledge, supporting the next generation of science- and data-driven climate risk management instruments, projects, and global initiatives. Through the lens of three use cases, we illustrate what data quality means for different applications, the implications of choosing, aggregating, and combining different climate datasets, and the potential consequences of dataset and baseline choice on decision-making. Choosing different datasets often leads to trade-offs that decision-makers need to be aware of, for instance, between the spatial resolution of a dataset and length of the data record. Here, we discuss the implications of selecting the appropriate time step, resolution, and dataset and provide examples of how these choices affect the identification of societally relevant anomalies.
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BibTeX
@article{brown2026decoding,
title = {{Decoding climate anomalies—From scientifically sound to societally relevant}},
author = {Molly E. Brown and Elena Tarnavsky and Markus Enenkel},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = oct,
doi = {10.1073/pnas.2321154122},
url = {https://doi.org/10.1073/pnas.2321154122},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Decoding climate anomalies—From scientifically sound to societally relevant
AU - Molly E. Brown
AU - Elena Tarnavsky
AU - Markus Enenkel
PY - 2026
DA - 2026/10/26
JO - Proceedings of the National Academy of Sciences of the United States of America
DO - 10.1073/pnas.2321154122
UR - https://doi.org/10.1073/pnas.2321154122
AB - As climate impacts worsen, decision-makers, researchers, and public sector institutions increasingly need reliable, relevant, and appropriate information about emerging weather and climate anomalies. However, with the ever-expanding choice of datasets based on ground observations, satellite data, and models, it is critical to not only use the best dataset for a specific decision-making need but to avoid misinforming and even derailing decision-making due to dataset selection. The main objective of this perspective is to illustrate the importance of understanding datasets used to translate climate data into actionable knowledge, supporting the next generation of science- and data-driven climate risk management instruments, projects, and global initiatives. Through the lens of three use cases, we illustrate what data quality means for different applications, the implications of choosing, aggregating, and combining different climate datasets, and the potential consequences of dataset and baseline choice on decision-making. Choosing different datasets often leads to trade-offs that decision-makers need to be aware of, for instance, between the spatial resolution of a dataset and length of the data record. Here, we discuss the implications of selecting the appropriate time step, resolution, and dataset and provide examples of how these choices affect the identification of societally relevant anomalies.
SN - 0027-8424
SN - 1091-6490
ER -