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Updated: Jun 20, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Façade-Level Monitoring of CO₂ Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

Incorporating model quality information in climate change detection and attribution studies.

B D Santer¹, K E Taylor, P J Gleckler

¹Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA. santer1@llnl.gov

Proceedings of the National Academy of Sciences of the United States of America

|August 27, 2009

Summary

Climate models

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Using Generative Art to Convey Past and Future Climate Transitions

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Published on: March 31, 2023

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Last Updated: Jun 20, 2026

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Façade-Level Monitoring of CO₂ Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Area of Science:

Climate science
Atmospheric physics

Background:

Detection and attribution (D&A) studies identify human influence on climate.
Previous studies used pooled climate model results equally, regardless of model skill.

Purpose of the Study:

To assess if water vapor detection and attribution results are sensitive to climate model quality.
To determine if model skill impacts the identification of anthropogenic water vapor fingerprints.

Main Methods:

Selected "top 10" and "bottom 10" climate models based on skill metrics (mean state, annual cycle, El Niño variability).
Repeated D&A analysis using subsets of models with varying skill levels.
Compared D&A results from pooled models versus skill-screened model groups.

Main Results:

Water vapor fingerprint detection is insensitive to climate model uncertainties.
Anthropogenic water vapor signals are governed by well-simulated physical processes.
Model quality screening did not affect the identification of human influence on water vapor.

Conclusions:

The human fingerprint on water vapor is robust and detectable regardless of climate model skill.
Basic physical processes driving water vapor changes are accurately represented in most climate models.
Confidence in detecting anthropogenic water vapor changes is high, unaffected by model selection criteria.