A Comprehensive Evaluation of Black Carbon in Snow and Its Radiative Forcing in CMIP5 and CMIP6 Models Based on Global Field Observations.

Black carbon in snow (BCS) is a crucial parameter in Earth System modeling, as it influences global radiative balance. Here, simulated BCS from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) that provided BCS as a model output were evaluated. In comparison with global BCS observations, CMIP5/6 models successfully reproduced long‐term historical trends linked to human activities, but struggled capturing decadal variability caused by natural climate variability. CMIP6 models NorESM2‐MM, NorESM2‐LM, and TaiESM1 yielded the most accurate simulations of BCS concentration with modest overestimation of <50%, while the four CESM2 models underestimated concentrations by up to ∼80%. These errors effectively balanced for the CMIP6 multi‐model ensemble mean (MME), which had a relative error (RE) of −37%. However, the CMIP5 MME was less reliable due to extreme overestimation by up to 8,000% in the three MIROC models. The significant BCS concentration errors in the MIROC and CESM2 models were linked mainly to errors in handling of BC in snow processes. Conversely, marked improvements in NorESM, the only common to both CMIP5 and CMIP6, were due to improved simulation of black carbon deposition. BCS errors significantly impacted radiative forcing estimates, particularly at the poles, where model errors reached several thousandfold. CMIP6 exhibited superior results compared to CMIP5, achieving global MME RE of −33% in radiative forcing estimates. However, it's worth noting BCS output is currently limited, with only seven models available for each of CMIP5 and CMIP6 here. Additional models simulating BCS are desirable in the next CMIP generations. Plain Language Summary: Black carbon in snow (BCS) has important implications for snow albedo reduction, solar radiation absorption, and snow melt acceleration on the Earth. However, model performance in BCS simulation remains unclear in the widely used Coupled Model Intercomparison Project phases 5 and 6 (CMIP5/6). In this study, CMIP5 and CMIP6 models providing BCS information were evaluated against a global data set of BCS observations from ice cores and snow samples. Results indicate that both CMIP generations successfully reproduce long‐term trends in BCS but fail to capture the decadal variability. Marked differences exist in modeled BCS concentrations, with the best‐performing models (NorESM2‐LM, NorESM2‐MM, and TaiESM1) overestimating concentrations by <50%, while the three worst‐performing MIROC models yield extreme overestimation. Such discrepancies stem primarily from the post‐depositional treatment of black carbon in snow, which influences the estimation of climate‐warming effects of BCS. Key Points: The global pattern of black carbon in snow (BCS) is effectively simulated by CMIP5/6 models, but large errors remainPost‐depositional snow processes contribute most to the large simulated‐BCS errorsBCS‐induced radiative forcing in Antarctica is poorly simulated by CMIP5/6 models with an overestimation of ∼200,000% and 144%, respectively [ABSTRACT FROM AUTHOR]