Global Cloud-Resolving Models

Satoh, Masaki; Stevens, Björn; Judt, Falko; Khairoutdinov, Marat; Lin, Shian‐Jiann; Putman, William M.; Dueben, Peter

doi:10.1007/s40641-019-00131-0

Cited by 226 publications

(198 citation statements)

References 138 publications

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“…Ultimately, we hope the tool summarized here can be deployed across the emerging hierarchy of global cloud resolving models (Satoh et al, ) to help clarify their intrinsic thermodynamics. Our spectral framework can be generalized to spatially limited domains by choosing a transform insensitive to nonperiodic boundaries, such as the discrete cosine transform (e.g., Denis et al, ; Selz et al, ).…”

Section: Resultsmentioning

confidence: 99%

“…Ultimately, we hope the tool summarized here can be deployed across the emerging hierarchy of global cloud resolving models (Satoh et al, 2019) to help clarify their intrinsic thermodynamics. Our spectral framework can be generalized to spatially limited domains by choosing a transform insensitive to nonperiodic Tom Beucler is supported by NSF Grants AGS-1520683 and OAC-1835769, Tristan Abbott and Timothy Cronin are supported by NSF Grants AGS-1740533 and AGS-1623218, and Mike Pritchard is supported by NSF Grant AGS-1734164 and DOE Grant DE-SC0012152.…”

Section: Resultsmentioning

confidence: 99%

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Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator

Beucler

Abbott²,

Cronin³

et al. 2019

Geophysical Research Letters

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Idealized convection‐permitting simulations of radiative‐convective equilibrium have become a popular tool for understanding the physical processes leading to horizontal variability of tropical water vapor and rainfall. However, the applicability of idealized simulations to nature is still unclear given that important processes are typically neglected, such as lateral water vapor advection by extratropical intrusions, or interactive ocean coupling. Here, we exploit spectral analysis to compactly summarize the multiscale processes supporting convective aggregation. By applying this framework to high‐resolution reanalysis data and satellite observations in addition to idealized simulations, we compare convective‐aggregation processes across horizontal scales and data sets. The results affirm the validity of the radiative‐convective equilibrium simulations as an analogy to the real world. Column moist static energy tendencies share similar signs and scale selectivity in convection‐permitting models and observations: Radiation increases variance at wavelengths above 1,000 km, while advection damps variance across wavelengths, and surface fluxes mostly reduce variance between 1,000 and 10,000 km.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator

Beucler

Abbott²,

Cronin³

et al. 2019

Geophysical Research Letters

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“…Water vapor, liquid cloud, ice cloud, rain, snow, and graupel were simulated using a single‐moment bulk cloud microphysics scheme (NSM6). The use of 14 km horizontal resolution without convection parameterization is justified in Satoh et al (). We use the NICAM AMIP‐type simulation for the historical period from June 1978 to December 2009 and future period from June 2074 to December 2105 under A1B scenario (Kodama et al, ; Satoh et al, ).…”

Section: Methodsmentioning

confidence: 99%

Precipitation Probability and Its Future Changes From a Global Cloud‐Resolving Model and CMIP6 Simulations

Kodama

2020

JGR Atmospheres

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This study investigates precipitation probability and its future changes using simulations from the Non‐hydrostatic Icosahedral Atmospheric Model (NICAM), a global cloud‐resolving model, and Coupled Model Intercomparison Project Phase 6 (CMIP6) models. For the precipitation probability comparison between models and Tropical Rainfall Measuring Mission (TRMM) during 1998–2008, the difference is greatly reduced by regridding to a common coarser resolution, but the difference signs largely remain. Both NICAM and CMIP6 model ensemble mean show less global‐mean nonprecipitation fractions than observations, but NICAM agrees better with observations. For global‐mean light precipitation probability, NICAM (CMIP6 model ensemble mean) is smaller (larger) than TRMM. NICAM shows higher heavy precipitation probability than TRMM in native resolutions, but it is close to TRMM and falls in the CMIP6 model range after regridding. For the precipitation probability changes from 1979–2008 to 2075–2104, NICAM predicts an increase in nonprecipitation and extreme heavy (>100 mm/day) precipitation probability but a slight decrease in the probability for precipitation <100 mm/day. After regridding to 2.5o, the precipitation intensity where the probability starts to increase becomes ~20 mm/day and the relative increases in nonprecipitation and heavy precipitation probability become much larger. Future heavy precipitation probability relative increases from CMIP6 models are much larger than NICAM at their native resolutions, but NICAM falls in the CMIP6 model range after regridding, indicating robust future increase (decrease) in heavy (light) precipitation probability regardless of underlying convection treatments. Discrepancy, however, exists in the projected nonprecipitation fraction changes: NICAM shows that nonprecipitation probability will increase over most of low and middle latitudes where CMIP6 models show less increases and even decreases.

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“…Initial condition uncertainty is addressed by perturbing the initial conditions of ensemble members, for example, by selecting directions of optimal perturbation growth using singular vectors (Buizza & Palmer, Despite the historical disconnect between the weather and climate prediction communities, the boundaries between weather and climate prediction are somewhat artificial (Hurrell et al, 2009;Palmer et al, 2008;Shapiro et al, 2010). This disconnect is challenged by recent advances in prediction on time scales from weather to subseasonal-to-seasonal and decadal by operational weather forecasting centers around the world (Moncrieff et al, 2007;Vitart & Robertson, 2012) and the ability of global cloud-resolving models to both forecast the weather and simulate the long-term climate (Crueger et al, 2018;Satoh et al, 2019;Zangl et al, 2015). Nonlinearities in the climate system lead to an upscale transfer of energy (and therefore error) from smaller to larger scales (Lorenz, 1969;Palmer, 2001;Tribbia & Baumhefner, 2004).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Gagne

Christensen

Subramanian

et al. 2020

J Adv Model Earth Syst

124

110

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Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and subgrid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate time scales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both time scales, and the networks closely reproduce the spatiotemporal correlations and regimes of the Lorenz '96 system. We also find that, in general, those models which produce skillful forecasts are also associated with the best climate simulations.

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Global Cloud-Resolving Models

Cited by 226 publications

References 138 publications

Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator

Comparing Convective Self‐Aggregation in Idealized Models to Observed Moist Static Energy Variability Near the Equator

Precipitation Probability and Its Future Changes From a Global Cloud‐Resolving Model and CMIP6 Simulations

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

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