Climate processes

Abstract:

Anthropogenic emissions of black carbon (BC) aerosols are generally thought to warm the climate. However, the magnitude of this warming remains highly uncertain due to limited knowledge of BC sources; optical properties; and atmospheric processes such as transport, removal, and cloud interactions. Here, we assess and constrain estimates of the historical warming influence of BC using recent observations and emission inventories. Based on simulations from four climate models, we show that the current global mean surface temperature change from anthropogenic BC due to aerosol-radiation interaction spans a factor of three—from +0.02 ± 0.02 K to +0.06 ± 0.05 K. Rapid atmospheric adjustments reduce the instantaneous radiative forcing by nearly 50% (multi-model mean), substantially lowering the net warming. Yet, recent satellite constraints suggest a stronger effect, highlighting the need for a more comprehensive reassessment of BC’s climate influence.

Abstract:

Realistically representing deep atmospheric convection is important for accurate numerical weather and climate simulations. However, parameterizing where and when deep convection occurs (“triggering”) is a well-known source of model uncertainty. Most triggers parameterize convection deterministically, without considering the uncertainty in the convective state as a stochastic process. In this study, we develop a machine learning model, a random forest, that predicts the probability of deep convection, and then apply clustering of SHAP values, an explainable machine learning method, to characterize the uncertainty of convective events. The model uses observed large-scale atmospheric variables from the Atmospheric Radiation Measurement constrained variational analysis dataset over the Southern Great Plains, US. The analysis of feature importance shows which mechanisms driving convection are most important, with large-scale vertical velocity providing the highest predictive power for more certain, or easier to predict, convective events, followed by the dynamic generation rate of dilute convective available potential energy. Predictions of uncertain, or harder to predict, convective events instead rely more on other features such as precipitable water or low-level temperature. The model outperforms conventional convective triggers. This suggests that probabilistic machine learning models can be used as stochastic parameterizations to improve the occurrence of convection in weather and climate models in the future.