No abstract

Despite the unequal partitioning of land and aerosol sources between the hemispheres, Earth’s albedo is observed to be persistently symmetric about the equator. This symmetry is determined by the compensation of clouds to the clear-sky albedo. Here, the variability of this inter-hemispheric albedo symmetry is explored by decomposing observed radiative fluxes in the CERES EBAF satellite data record into components reflected by the atmosphere, clouds, and the surface. We find that the degree of inter-hemispheric albedo symmetry has not changed significantly throughout the observational record. The variability of the inter-hemispheric difference in reflected solar radiation (asymmetry) is strongly determined by tropical and subtropical cloud cover, particularly those related to non-neutral phases of the El Niño-Southern Oscillation (ENSO). As the ENSO is the most significant source of interannual variability in reflected radiation on a global scale, this underscores the inter-hemispheric albedo symmetry as a robust feature of Earth’s current annual mean climate. Comparing this feature in observations with simulations from coupled models reveals that the degree of modeled albedo symmetry is mostly dependent on biases in reflected radiation in the midlatitudes, and that models that overestimate its variability the most have larger biases in reflected radiation in the tropics. The degree of model albedo symmetry is improved when driven with historical sea surface temperatures, indicating that the degree of symmetry in Earth’s albedo is dependent on the representation of cloud responses to coupled ocean-atmosphere processes.

Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi‐model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5, and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and ancestry weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near‐surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed ancestry and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases.