Computational advances have made atmospheric modeling at convection‐permitting (≤4 km) grid spacings increasingly feasible. These simulations hold great promise in the projection of climate change impacts including rainfall and flood extremes. The relatively short model runs that are currently feasible, however, inhibit the assessment of the upper tail of rainfall and flood quantiles using conventional statistical methods. Stochastic storm transposition (SST) and process‐based flood frequency analysis are two approaches that together can help to mitigate this limitation. SST generates large numbers of extreme rainfall scenarios by temporal resampling and geospatial transposition of rainfall fields from relatively short data sets. Coupling SST with process‐based flood frequency analysis enables exploration of flood behavior at a range of spatial and temporal scales. We apply these approaches with outputs of 13‐year simulations of regional climate to examine changes in extreme rainfall and flood quantiles up to the 500‐year recurrence interval in a medium‐sized watershed in the Midwestern United States. Intensification of extreme precipitation across a range of spatial and temporal scales is identified in future climate; changes in flood magnitudes depend on watershed area, with small watersheds exhibiting the greatest increases due to their limited capacity to attenuate flood peaks. Flood seasonality and snowmelt are predicted to be earlier in the year under projected warming, while the most extreme floods continue to occur in early summer. Findings highlight both the potential and limitations of convection‐resolving climate models to help understand possible changes in rainfall and flood frequency across watershed scales.
- NSF-PAR ID:
- 10299896
- Date Published:
- Journal Name:
- CI2020
- Page Range / eLocation ID:
- 98 to 105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract. The tropical tropopause layer (TTL) is a sea of vertical motions. Convectively generated gravity waves create vertical winds on scales of a few to thousands of kilometers as they propagate in a stable atmosphere. Turbulence from gravity wave breaking, radiatively driven convection, and Kelvin–Helmholtz instabilities stirs up the TTL on the kilometer scale. TTL cirrus clouds, which moderate the water vapor concentration in the TTL and stratosphere, form in the cold phases of large-scale (> 100 km) wave activity. It has been proposed in several modeling studies that small-scale (< 100 km) vertical motions control the ice crystal number concentration and the dehydration efficiency of TTL cirrus clouds. Here, we present the first observational evidence for this. High-rate vertical winds measured by aircraft are a valuable and underutilized tool for constraining small-scale TTL vertical wind variability, examining its impacts on TTL cirrus clouds, and evaluating atmospheric models. We use 20 Hz data from five National Aeronautics and Space Administration (NASA) campaigns to quantify small-scale vertical wind variability in the TTL and to see how it varies with ice water content, distance from deep convective cores, and height in the TTL. We find that 1 Hz vertical winds are well represented by a normal distribution, with a standard deviation of 0.2–0.4 m s−1. Consistent with a previous observational study that analyzed two out of the five aircraft campaigns that we analyze here, we find that turbulence is enhanced over the tropical west Pacific and within 100 km of convection and is most common in the lower TTL (14–15.5 km), closer to deep convection, and in the upper TTL (15.5–17 km), further from deep convection. An algorithm to classify turbulence and long-wavelength (5 km < λ < 100 km) and short-wavelength (λ < 5 km) gravity wave activity during level flight legs is applied to data from the Airborne Tropical TRopopause EXperiment (ATTREX). The most commonly sampled conditions are (1) a quiescent atmosphere with negligible small-scale vertical wind variability, (2) long-wavelength gravity wave activity (LW GWA), and (3) LW GWA with turbulence. Turbulence rarely occurs in the absence of gravity wave activity. Cirrus clouds with ice crystal number concentrations exceeding 20 L−1 and ice water content exceeding 1 mg m−3 are rare in a quiescent atmosphere but about 20 times more likely when there is gravity wave activity and 50 times more likely when there is also turbulence, confirming the results of the aforementioned modeling studies. Our observational analysis shows that small-scale gravity waves strongly influence the ice crystal number concentration and ice water content within TTL cirrus clouds. Global storm-resolving models have recently been run with horizontal grid spacing between 1 and 10 km, which is sufficient to resolve some small-scale gravity wave activity. We evaluate simulated vertical wind spectra (10–100 km) from four global storm-resolving simulations that have horizontal grid spacing of 3–5 km with aircraft observations from ATTREX. We find that all four models have too little resolved vertical wind at horizontal wavelengths between 10 and 100 km and thus too little small-scale gravity wave activity, although the bias is much less pronounced in global SAM than in the other models. We expect that deficient small-scale gravity wave activity significantly limits the realism of simulated ice microphysics in these models and that improved representation requires moving to finer horizontal and vertical grid spacing.more » « less
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Abstract Kilometer-scale climate model simulations are useful tools to investigate past and future changes in extreme precipitation, particularly in mountain regions, where convection is influenced by complex topography and land–atmosphere interactions. In this study, we evaluate simulations of a flood-producing mesoscale convective system (MCS) downstream of the Tibetan Plateau (TP) in the Sichuan basin from a kilometer-scale multimodel and multiphysics ensemble. The aim is to better understand the physical processes that need to be correctly simulated for successfully capturing downstream MCS formation. We assess how the ensemble members simulate these processes and how sensitive the simulations are to different model configurations. The preceding vortex evolution over the TP, its interaction with the jet stream, and water vapor advection into the basin are identified as key processes for the MCS formation. Most modeling systems struggle to capture the interaction between the vortex and jet stream, and perturbing the model physics has little impact, while constraining the large-scale flow by spectral nudging improves the simulation. This suggests that an accurate representation of the large-scale forcing is crucial to correctly simulate the MCS and associated precipitation. To verify whether the identified shortcomings systematically affect the MCS climatology in longer-term simulations, we evaluate a 1-yr WRF simulation and find that the seasonal cycle and spatial distribution of MCSs are reasonably well captured and not improved by spectral nudging. While the simulations of the MCS case highlight challenges in extreme precipitation forecasting, we conclude that these challenges do not systematically affect simulated climatological MCS characteristics.
Significance Statement Convective storm systems in mountain regions are not well understood, because the spatial resolution in conventional regional climate models is too coarse to resolve relevant processes. Here, we evaluate high-resolution climate model simulations of a storm system on the downwind side of the Tibetan Plateau. Understanding which models and model setups work well to represent this type of storm system is important because high-resolution models can help us understand mechanisms of storm formation in mountain regions and how climate change will affect these. A key finding is that most of the models struggle to capture the selected storm case, while a 1-yr simulation shows that the general statistics of storm systems around the Tibetan Plateau are still reasonably well captured.
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Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior belief, we show that generative models alone are not sufficient to prevent shortcut learning, despite an incentive to recover a more comprehensive representation of the data than discriminative approaches. However, we observe that shortcuts are preferentially encoded with minimal information, a fact that generative models can exploit to mitigate shortcut learning. In particular, we propose Chroma-VAE, a two-pronged approach where a VAE classifier is initially trained to isolate the shortcut in a small latent subspace, allowing a secondary classifier to be trained on the complementary, shortcut-free latent subspace. In addition to demonstrating the efficacy of Chroma-VAE on benchmark and real-world shortcut learning tasks, our work highlights the potential for manipulating the latent space of generative classifiers to isolate or interpret specific correlations.more » « less
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Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3429309.3429324
