The ability to distinguish observed climate change from naturally arising climate variability is one of the most fundamental findings in climate science. Climate change emergence is a key statistic used to quantify the impact of human activities on climate and inform climate change mitigation strategies. Previous studies determining when climate change has emerged, however, do not account for uncertainty in historical observations or climate models. Here, we show that the emergence of regional warming is early, widespread, and robust to observational and model uncertainty. Warming has emerged over more than 90% of the global land surface as of 2020, and had emerged over more than 50% of the earth's land surface by 2000. Emergence occurs despite observational and model uncertainty creating delays of more than 10 years over 75% of the land surface. These conclusions demonstrate that a complete uncertainty accounting is necessary for robust detection of climate change emergence.

Ice nucleation in mixed-phase clouds has recently been identified as a critical factor in projections of future climate. Here we explore how this process influences climate sensitivity using the Community Earth System Model 2 (CESM2). We find that ice nucleation affects simulated cloud feedbacks over most regions and levels of the troposphere, not just extratropical low clouds. Ice nucleation’s impact on climate sensitivity is found to primarily operate through this process’s role setting global-scale cloud phase. Conversely, whether ice nucleation is treated as aerosol-sensitive is of limited importance. In satellite-constrained model experiments, dissimilar ice nucleation realizations all result in a strongly positive total cloud feedback, as in the default CESM2. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation, due partly to a model issue. Our findings suggest that this contributed to increased climate sensitivity by reducing global cloud phase bias, resulting in more realistic mixed-phase clouds.

Mixed-phase clouds play an important role in determining Arctic warming, but are parametrized in models and difficult to constrain with observations. We use two satellite-derived cloud phase metrics to investigate the vertical structure of Arctic clouds in two global climate models that use the Community Atmosphere Model version 6 (CAM6) atmospheric component. We report a model error limiting ice nucleation, produce a set of Arctic-constrained model runs by adjusting model microphysical variables to match the cloud phase metrics, and evaluate cloud feedbacks for all simulations. Models in this small ensemble uniformly overestimate total cloud fraction in the summer, but have variable representation of cloud fraction and phase in the winter and spring. By relating modelled cloud phase metrics and changes in low-level liquid cloud amount under warming to longwave cloud feedback, we show that mixed-phase processes mediate the Arctic climate by modifying how wintertime and springtime clouds respond to warming.