Emergent constraints for uncertainty reduction in climate projections

Studying longwave radiation and surface air temperature is essential for understanding Earth's energy budget and climate dynamics. These factors are fundamental for spotting trends, forecasting future situations, and evaluating how human actions affect global warming, making their accurate values very important. In this study, we analyze the uncertainties in surface downward longwave radiation, average monthly values of absorbed longwave radiation, and average monthly surface air temperature obtained from advanced models. The combination of the emergent constraint approach and natural time analysis was used for the first time to study the variability of these climate parameters and explore ways to reduce uncertainty. The data analysis reveals significant findings that enhance the reliability of future climate simulations, especially regarding the forecast of the atmospheric greenhouse effect components for the next two years. • An emergent constraint technique, based on the longwave radiation and surface air temperature data, has been developed to improve future projections. • The proposed emergent constraint tool demonstrates the reliability of nowcasting future surface temperature distributions and related parameters • The results obtained highlighted the importance of advanced modeling techniques in improving our understanding of climate dynamics.