Search for: All records

Abstract The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM_2.5concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from prescribed fires, which is critical in planning the prescribed fires’ location and time, at hourly to daily time scales remains a challenging problem. In this paper, we introduce a spatio-temporal graph neural network (GNN)-based forecasting model for hourly PM_2.5predictions across California. Utilizing a two-step approach, we use our forecasting model to predict the net and ambient PM_2.5concentrations, which are used to estimate wildfire contributions. Integrating the GNN-based PM_2.5forecasting model with simulations of historically prescribed fires, we propose a novel framework to forecast their air quality impact. This framework determines that March is the optimal month for implementing prescribed fires in California and quantifies the potential air quality trade-offs involved in conducting more prescribed fires outside the peak of the fire season. 

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation ( R 2 ∼ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability ( R 2 ∼ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes. 

Abstract. Ice growth from vapor deposition is an important process for the evolution of cirrus clouds, but the physics of depositional ice growth at the low temperatures (<235 K) characteristic of the upper troposphere and lower stratosphere is not well understood. Surface attachment kinetics, generally parameterized as a deposition coefficient αD, control ice crystal habit and also may limit growth rates in certain cases, but significant discrepancies between experimental measurements have not been satisfactorily explained. Experiments on single ice crystals have previously indicated the deposition coefficient is a function of temperature and supersaturation, consistent with growth mechanisms controlled by the crystal's surface characteristics. Here we use observations from cloud chamber experiments in the Aerosol Interactions and Dynamics in theAtmosphere (AIDA) aerosol and cloud chamber to evaluate surface kinetic models in realistic cirrus conditions. These experiments have rapidly changing temperature, pressure, and ice supersaturation such that depositional ice growth may evolve from diffusion limited to surface kinetics limited over the course of a single experiment. In Part 1, we describe the adaptation of a Lagrangian parcel model with the Diffusion Surface Kinetics Ice Crystal Evolution (DiSKICE) model (Zhang and Harrington, 2014) to the AIDA chamber experiments. We compare the observed ice water content and saturation ratios to that derived under varying assumptions for ice surface growth mechanisms for experiments simulating ice clouds between 180 and 235 K and pressures between 150 and 300 hPa. We found that both heterogeneous and homogeneous nucleation experiments at higher temperatures (>205 K) could generally be modeled consistently with either a constant deposition coefficient or the DiSKICE model assuming growth on isometric crystals via abundant surface dislocations. Lower-temperature experiments showed more significant deviations from any depositional growth model, with different ice growth rates for heterogeneous and homogeneous nucleation experiments. 

Abstract. High-altitude cirrus clouds are climatically important: their formationfreeze-dries air ascending to the stratosphere to its final value, and theirradiative impact is disproportionately large. However, their formation andgrowth are not fully understood, and multiple in situ aircraft campaigns haveobserved frequent and persistent apparent water vapor supersaturations of5 %–25 % in ultracold cirrus (T<205 K), even in the presence of iceparticles. A variety of explanations for these observations have been putforth, including that ultracold cirrus are dominated by metastable ice whosevapor pressure exceeds that of hexagonal ice. The 2013 IsoCloud campaign atthe Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud andaerosol chamber allowed explicit testing of cirrus formation dynamics atthese low temperatures. A series of 28 experiments allows robust estimationof the saturation vapor pressure over ice for temperatures between 189 and235 K, with a variety of ice nucleating particles. Experiments are rapidenough (∼10 min) to allow detection of any metastable ice that mayform, as the timescale for annealing to hexagonal ice is hours or longer overthe whole experimental temperature range. We show that in all experiments,saturation vapor pressures are fully consistent with expected values forhexagonal ice and inconsistent with the highest values postulated formetastable ice, with no temperature-dependent deviations from expectedsaturation vapor pressure. If metastable ice forms in ultracold cirrusclouds, it appears to have a vapor pressure indistinguishable from that ofhexagonal ice to within about 4.5 %.