Multidimensional Analysis of Climate Change: Scientific Facts, Impact Assessment, and Response Pathways—A Literature Review Based on Observational Records and Quantitative Models

Human activities are pushing the global climate system towards pronounced change whose principal axis is warming. Organised around the framework “scientific facts—impact assessment—response pathways”, this review addresses three progressively nested questions: (1) whether observational indicators drawn from different sources yield warming signals that are consistent in direction and comparable in magnitude; (2) on what chain of evidence and at what confidence level observed changes can be attributed to anthropogenic forcing, and where the respective boundaries of the probability-ratio and attributable-fraction frameworks lie; and (3) whether the responses of ecological, agricultural and economic systems are smooth or exhibit thresholds, and how the location of those thresholds bears upon the trade-off between carbon budgets and adaptation investment. Models were not selected to compete on predictive accuracy; three other criteria were applied instead—parameter transparency, independent reproducibility, and outputs that map directly onto policy variables. Eight simplified quantitative models were accordingly included: the logarithmic parameterisation of radiative forcing, climate sensitivity, probability-ratio attribution, the quadratic sea-level trend, the linear carbon–ice response, the piecewise-linear crop response, the inverted-U macroeconomic response, and the remaining carbon budget. These are used to check orders of magnitude and to locate thresholds, and their respective conditions of application and boundaries of failure are set out explicitly. The main findings are as follows: global surface temperature in 2011–2020 was 1.09 °C above the pre-industrial level (likely range 0.95–1.20 °C); the IPCC assesses human influence on the climate system as “unequivocal”; increases in the probability of extreme-heat events are now detectable; ecological, food and macroeconomic systems exhibit nonlinear and asymmetric responses; and, under the constraint of the remaining carbon budget, global CO₂ emissions must reach net zero around the middle of this century. Two qualifications should be stated at the outset. First, this is a literature review, and all data used are published secondary aggregate data. Second, the eight models derive from independent datasets in different studies and cannot be treated as a like-for-like comparison of accuracy under a common data condition. Because the intervals estimated by different groups for key parameters such as ECS and TCRE overlap, findings are reported as best estimates with intervals, and no ranking of the models is attempted.

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Publication Details

Journal
Environment, social and governance.
Published
2026-10-30
DOI
https://doi.org/10.70267/esg.v3n4.7597
Primary Topic
Climate variability and models
Type
article
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article

Multidimensional Analysis of Climate Change: Scientific Facts, Impact Assessment, and Response Pathways—A Literature Review Based on Observational Records and Quantitative Models

Peiming Yang
Environment, social and governance.
Climate variability and models
article

Multidimensional Analysis of Climate Change: Scientific Facts, Impact Assessment, and Response Pathways—A Literature Review Based on Observational Records and Quantitative Models

Peiming Yang
article en

Abstract

Human activities are pushing the global climate system towards pronounced change whose principal axis is warming. Organised around the framework “scientific facts—impact assessment—response pathways”, this review addresses three progressively nested questions: (1) whether observational indicators drawn from different sources yield warming signals that are consistent in direction and comparable in magnitude; (2) on what chain of evidence and at what confidence level observed changes can be attributed to anthropogenic forcing, and where the respective boundaries of the probability-ratio and attributable-fraction frameworks lie; and (3) whether the responses of ecological, agricultural and economic systems are smooth or exhibit thresholds, and how the location of those thresholds bears upon the trade-off between carbon budgets and adaptation investment. Models were not selected to compete on predictive accuracy; three other criteria were applied instead—parameter transparency, independent reproducibility, and outputs that map directly onto policy variables. Eight simplified quantitative models were accordingly included: the logarithmic parameterisation of radiative forcing, climate sensitivity, probability-ratio attribution, the quadratic sea-level trend, the linear carbon–ice response, the piecewise-linear crop response, the inverted-U macroeconomic response, and the remaining carbon budget. These are used to check orders of magnitude and to locate thresholds, and their respective conditions of application and boundaries of failure are set out explicitly. The main findings are as follows: global surface temperature in 2011–2020 was 1.09 °C above the pre-industrial level (likely range 0.95–1.20 °C); the IPCC assesses human influence on the climate system as “unequivocal”; increases in the probability of extreme-heat events are now detectable; ecological, food and macroeconomic systems exhibit nonlinear and asymmetric responses; and, under the constraint of the remaining carbon budget, global CO₂ emissions must reach net zero around the middle of this century. Two qualifications should be stated at the outset. First, this is a literature review, and all data used are published secondary aggregate data. Second, the eight models derive from independent datasets in different studies and cannot be treated as a like-for-like comparison of accuracy under a common data condition. Because the intervals estimated by different groups for key parameters such as ECS and TCRE overlap, findings are reported as best estimates with intervals, and no ranking of the models is attempted.

Environment, social and governance.Vol. 3(4)
Changsha University of Science and Technology (CN)
Climate action
Openalex Percentile: Top 17%
Climate variability and models
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