Journal Article

Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity

Kuma, Peter, Jönsson, Aiden R., Bender, Frida A.‐M.

2023

Overview

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. Plain Language Summary Contemporary global climate models are developed by a large number of modeling groups internationally. Commonly, projections from multiple models are used together to calculate multi‐model means and quantify uncertainty. Because many of the models share parts of their computer code, algorithms and parametrization schemes, they are not independent. Overrepresented models can cause biases in multi‐model means, and uncertainty may be underestimated if model dependence is not taken into account. We document a full code genealogy of 167 models, of which 114 participated in the Coupled Model Intercomparison Project (CMIP) phases 3, 5, and 6, with a focus on the atmospheric component. We identify 12 main model families. We show that models in the same family often have similar estimates of key climate properties. We propose statistical weighting methods based on the model family and code relationship, and show that they can reconcile some of the difference in results between the two most recent CMIP phases. The weighting methods or a selection of independent models based on the genealogy can be used in model assessment studies to reduce the effects of model dependence. Key Points We reconstruct a code genealogy of 167 climate models with a focus on the atmospheric component and atmospheric physics All models originate from 12 main model families, and models in the same family often have similar climate feedbacks and sensitivity Proposed ancestry and family weighting can partly reconcile differences in means between the Coupled Model Intercomparison Project phases

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: Air temperature / Atmospheric models / Atmospheric physics / Climate / Climate change / Climate feedback / climate feedbacks / Climate models / Climate sensitivity / CMIP / code / Democracy / equilibrium climate sensitivity / Genealogy / General circulation models / Hypotheses / Intercomparison / model genealogy / Modelling / Physics / Software / Statistical methods / Surface temperature / Surface-air temperature relationships