Low Uncertainty Regional Climate Projections without Irrelevant Weather Details

Uncertainties in conventional (GCM) climate models, defined as the structural spread among com-peting models, have increased for the first time in the latest AR6 report despite an exponential increasein the modern computation power. The root problem is that these models are based in the weatherregime, that is, they spend unnecessary effort in calculating irrelevant weather details. This projectaims to produce precise regional projection using the Half Order Energy Balance Equation (HEBE): ahalf order fractional derivative generalization of the standard Energy Balance Equation (EBE). HEBEhas the advantage of being a direct consequence of the continuum heat equation combined with energy-conserving surface boundary conditions. A previous paper used Fractional EBE (FEBE) to model Earthclimate projections through 2100 on a global scale, and it yields significantly smaller uncertainty com-pared to the CMIP6 MME. This project builds on a similar methodology, enhancing climate projectionwith additional regional details and upgraded precision. The current results show that the parametricuncertainty in HEBE’s temperature response is smaller than the internal variability at most locations,at the exceptions of the high memory deep ocean regions near Pacific. HEBE’s regional hindcast ac-curately reproduces ERA5 2mT series’ deterministic and stochastic patterns of regional temperature.The global hindcast is also validated by various reanalysis datasets and instrumental records. Thedirect year to year relative uncertainty (ratio between 90% confidence interval and best estimate) isstable across time and marker scenarios, with most regions projecting values below 0.5 by 2100. On aglobal scale, the parametric uncertainty in HEBE’s response temperature is negligible (±0.03K by 2100using the SSP2-4.5 marker scenario). This effectively shows that HEBE’s projection is more precisethan its competitors even without taking period averages. The exceedingly low global uncertainty wasconstrained by the large amount of regional information when taking the global averages. It should benoted that the cited parametric uncertainty does not take into account systematic biases in HEBE andin the input datasets. The most important source should be any errors in the forcings, especially con-cerning aerosols. HEBE aims to provide a compelling and physically grounded alternative to complexdeterministic multi-model ensembles, offering a more precise, efficient, and interpretable means of pro-jecting regional climate changes in the coming century. This positions it as a potentially valuable toolfor policy-relevant projections and adaptation planning, thereby showing the pertinency of fractionalderivative and Bayesian framework in atmospheric sciences.