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Updated: Jun 3, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Published on: September 7, 2019

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基于模型的全球气候分类系统的不确定性地图

Andrés Navarro1, Andrés Merino2, Eduardo García-Ortega2

  • 1Earth and Space Sciences (ESS) Group, Institute of Environmental Sciences, University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n, 45071, Toledo, Spain. andres.navarro@uclm.es.

Scientific data
|January 8, 2025
PubMed
概括

来自全球气候模型 (GCM) 的气候分类系统 (CCS) 有局限性. 本研究使用CMIP6数据绘制了四个CCS的不确定性,帮助气候变化适应和缓解努力.

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
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