A research team led by Professor Ming Cai from Florida State University, in collaboration with researchers from Sun Yat-sen University, Peking University, and the Massachusetts Institute of Technology, recently published a groundbreaking paper in National Science Review . Titled “Principles-Based Adept Predictions of Global Warming from Climate Mean States”, the study introduces a novel framework that accurately predicts the magnitude and spatial pattern of global warming caused by greenhouse gas emissions, without relying on complex climate models or statistical analysis. For the first time, this study confirms that observed global warming is driven by human-caused greenhouse gas emissions, independently of these conventional approaches.
This new framework incorporates energy balance principles and the coupled atmosphere-surface thermal absorption and emission processes, modeled similarly to feedback loops in electrical circuits. It directly quantifies the amplification of external energy inputs through climate feedback processes, using the information from climate mean states. Unlike climate models, this approach bypasses the need for costly time integrations to equilibrium and instead directly and efficiently calculates the equilibrium response of the global climate system to external energy perturbations.
The observed global mean warming from 1980–2000 to 2000–2020 is 0.414 K, while the framework predicts a warming of 0.403 K based solely on observed CO₂ concentration changes, excluding natural climate variability and aerosols. In comparison, climate simulations by CMIP6 models overestimate the observed warming by 50%. Additionally, the new framework’s predictions have a smaller global mean absolute error compared to CMIP6 models. This study also tested the framework under two assumed CO₂ scenarios: a sudden quadrupling of CO₂ and a 1% annual increase in CO₂. The framework accurately reproduced the global warming projections for each of these scenarios from CMIP6 models, with smaller uncertainty in warming predictions compared to the models. These results highlight the new framework's reliability and broad applicability.
National Science Review
Computational simulation/modeling