Climate processes

Abstract:

Anthropogenic aerosols are a primary source of uncertainty in future climate projections. Changes to aerosol concentrations modify cloud radiative properties, radiative fluxes, and precipitation from the micro- to the global scale. Due to computational constraints, we have been unable to explicitly simulate cloud dynamics in global-scale simulations, leaving key processes, such as convective updrafts, parameterized. This has significantly limited our understanding of aerosol impacts on convective clouds and climate. However, new state-of-the-art climate models are capable of representing these scales. In this study, we used the kilometer-scale Icosahedral Nonhydrostatic (ICON) earth system model to explore the global-scale rapid response of clouds and precipitation to an idealized distribution of anthropogenic aerosol via aerosol-cloud interactions (ACI) and aerosol-radiation interactions (ARI). In our simulations over 30 days, we find that the aerosol impacts on clouds and precipitation exhibit strong regional dependence. The impact of ARI and ACI on clouds in isolation shows some consistent behavior, but the magnitude and additive nature of the effects are regionally dependent. Some regions are dominated by either ACI or ARI, whereas others behaved nonlinearly. This suggests that the findings of isolated case studies from regional simulations may not be globally representative; ARI and ACI cannot be considered independently and should both be interactively represented in modelling studies. We also observe pronounced diurnal cycles in the rapid response of cloud microphysical and radiative properties, which suggests the usefulness of using polar-orbiting satellites to quantify ACI and ARI may be more limited than presently assumed. The simulations highlight some limitations that need to be considered in future studies. Isolating kilometerscale aerosol responses from internal variability will require longer averaging periods or ensemble simulations. It would also be beneficial to use interactive aerosols and assess the sensitivity of the conclusions to the cloud microphysics scheme.

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.

Abstract:

Changes in “blue water”, which is the total supply of fresh water available for human extraction over land, are quite closely related to changes in runoff or equivalently precipitation minus evaporation, P − E $$ P-E $$ . This article examines how climate change‐driven recent past and future changes in the regional water cycle relate to blue water availability and changes in human blue water demand. Although at the largest scales theoretical and numerical model predictions are in broad agreement with observations, at continental scales and below models predict large ranges of possible future P − E $$ P-E $$ and runoff especially at the scale of individual river catchments and for shorter timescale subseasonal floods and droughts. Nevertheless, it is expected that the occurrence and severity of floods will increase and that of droughts may increase, possibly compounded by human‐driven non‐climatic changes such as changes in land use, dam water impoundment, irrigation and extraction of groundwater. Contemporary assessments predict that increases in 21st century human water extraction in many highly‐populated regions are unlikely to be sustainable given projections of future P − E $$ P-E $$ . To reduce uncertainty in future predictions, there is an urgent need to improve modeling of atmospheric, land surface and human processes and how these components are coupled. This should be supported by maintaining the observing network and expanding it to improve measurements of land surface, oceanic and atmospheric variables. This includes the development of satellite observations stable over multiple decades and suitable for building reanalysis datasets appropriate for model evaluation.