Toward Co‐Designed Earth System Models: Reflecting End‐User Priorities in Local Applications From a Modeler's Perspective

Abstract Earth System Models (ESM) are crucial for quantifying climate impacts across Earth's interconnected systems and supporting science‐based adaptation and mitigation. However, not including end‐users, especially decision‐makers representing communities vulnerable to climate change, can limit model utility, increase epistemic risks, and lead to information misuse in decision‐making. While the ESM community increasingly values broad community engagement, end‐users may not initially perceive models as useful for local planning. Co‐designing models with end‐users fosters two‐way learning: users better understand models and their outputs, while modelers gain insights into fine‐scale local processes like monitoring practices and management priorities. Higher‐level co‐design can lead to more customized, priority‐driven, and useful modeling products. Despite these benefits, modelers often struggle to initiate meaningful partnerships with local communities. Therefore, this paper explores model co‐design from the perspective of modelers. This study presents two case studies where modelers and social scientists collaborated with Indigenous communities' decision‐makers to reflect their priorities in model design and application. In the Arctic Rivers Project, high‐resolution climate and hydrology data sets for Alaska were developed with guidance from an Indigenous Advisory Council, using optimized, coupled land‐atmosphere models. In the Mid‐Klamath Project, we partnered with the Karuk Tribe's Department of Natural Resources to assess climate change and prescribed burning impacts on terrestrial hydrology in the Klamath River Basin. Drawing from these studies, we introduce a four‐level framework: (a) Co‐design Configuration; (b) Model Tuning; (c) Incorporate Contextual Knowledge; (d) Co‐develop New Model Functions. We aim to help researchers consider and compare co‐design across diverse modeling projects systematically and coherently.

Authors

Institutions

Publication Details

Journal
AGU Advances
Published
2025-12-01
DOI
https://doi.org/10.1029/2025av001921
Primary Topic
Sustainability and Climate Change Governance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Toward Co‐Designed Earth System Models: Reflecting End‐User Priorities in Local Applications From a Modeler's Perspective

Andrew J. Newman, Dylan Blaskey, K. N. Musselman, Joshua C. Koch et al.
AGU Advances
Sustainability and Climate Change Governance
article

Toward Co‐Designed Earth System Models: Reflecting End‐User Priorities in Local Applications From a Modeler's Perspective

Andrew J. Newman, Dylan Blaskey, K. N. Musselman, Joshua C. Koch, Yifan Cheng, Nicole M. Herman‐Mercer, Monica Ainhorn Morrison, Tvetene Carlson, R. Toohey, Daniel Sarna‐Wojcicki, Peyton A. Thomas, Edda Mutter, Cassandra M. Brooks, Jenessa Tlen, Cleo Woelfle‐Hazard
article en

Abstract

Abstract Earth System Models (ESM) are crucial for quantifying climate impacts across Earth's interconnected systems and supporting science‐based adaptation and mitigation. However, not including end‐users, especially decision‐makers representing communities vulnerable to climate change, can limit model utility, increase epistemic risks, and lead to information misuse in decision‐making. While the ESM community increasingly values broad community engagement, end‐users may not initially perceive models as useful for local planning. Co‐designing models with end‐users fosters two‐way learning: users better understand models and their outputs, while modelers gain insights into fine‐scale local processes like monitoring practices and management priorities. Higher‐level co‐design can lead to more customized, priority‐driven, and useful modeling products. Despite these benefits, modelers often struggle to initiate meaningful partnerships with local communities. Therefore, this paper explores model co‐design from the perspective of modelers. This study presents two case studies where modelers and social scientists collaborated with Indigenous communities' decision‐makers to reflect their priorities in model design and application. In the Arctic Rivers Project, high‐resolution climate and hydrology data sets for Alaska were developed with guidance from an Indigenous Advisory Council, using optimized, coupled land‐atmosphere models. In the Mid‐Klamath Project, we partnered with the Karuk Tribe's Department of Natural Resources to assess climate change and prescribed burning impacts on terrestrial hydrology in the Klamath River Basin. Drawing from these studies, we introduce a four‐level framework: (a) Co‐design Configuration; (b) Model Tuning; (c) Incorporate Contextual Knowledge; (d) Co‐develop New Model Functions. We aim to help researchers consider and compare co‐design across diverse modeling projects systematically and coherently.

AGU AdvancesVol. 6(6)
NSF National Center for Atmospheric Research (US), United States Geological Survey (US), University of South Carolina (US), Institute of Arctic and Alpine Research (US), University of Colorado Boulder (US), Life Services (United States) (US), Research Applications (United States) (US), NSF NCAR Climate and Global Dynamics Laboratory (US), Louisiana Department of Natural Resources (US), Council of Athabascan Tribal Governments (US), Alaska Climate Adaptation Science Center, U.S. Geological Survey, Alaska Science Center, University at Buffalo, State University of New York (US), University of California, Berkeley (US), Social Sciences and Humanities Research Council (CA)
Openalex Percentile: Top 64%
Sustainability and Climate Change Governance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.