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

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Abstract 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.

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