More Like this

1. Precipitation Extremes and Their Modulation by Convective Organization in RCEMIP

   https://doi.org/10.1029/2024MS004535  

   O’Donnell, Graham L; Wing, Allison A (November 2024, Journal of Advances in Modeling Earth Systems)

   Abstract We examine the influence of convective organization on extreme tropical precipitation events using model simulation data from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP). At a given SST, simulations with convective organization have more intense precipitation extremes than those without it at all scales, including instantaneous precipitation at the grid resolution (3 km). Across large‐domain simulations with convective organization, models with explicit convection exhibit better agreement in the response of extreme precipitation rates to warming than those with parameterized convection. Among models with explicit convection, deviations from the Clausius‐Clapeyron scaling of precipitation extremes with warming are correlated with changes in organization, especially on large spatiotemporal scales. Though the RCEMIP ensemble is nearly evenly split between CRMs which become more and less organized with warming, most of the models which show increased organization with warming also allow super‐CC scaling of precipitation extremes. We also apply an established precipitation extremes scaling to understand changes in the extreme condensation events leading to extreme precipitation. Increased organization leads to greater increases in precipitation extremes by enhancing both the dynamic and implied efficiency contributions. We link these contributions to environmental variables modified by the presence of organization and suggest that increases in moisture in the aggregated region may be responsible for enhancing both convective updraft area fraction and precipitation efficiency. By leveraging a controlled intercomparison of models with both explicit and parameterized convection, this work provides strong evidence for the amplification of tropical precipitation extremes and their response to warming by convective organization. 

   more » « less
2. RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project

   https://doi.org/10.5194/gmd-17-6195-2024  

   Wing, Allison A; Silvers, Levi G; Reed, Kevin A (January 2024, Geoscientific Model Development)

   Abstract. The radiative–convective equilibrium (RCE) model intercomparison project (RCEMIP) leveraged the simplicity of RCE to focus attention on moist convective processes and their interactions with radiation and circulation across a wide range of model types including cloud-resolving models (CRMs), general circulation models (GCMs), single-column models, global cloud-resolving models, and large-eddy simulations. While several robust results emerged across the spectrum of models that participated in the first phase of RCEMIP (RCEMIP-I), two points that stand out are (1) the strikingly large diversity in simulated climate states and (2) the strong imprint of convective self-aggregation on the climate state. However, the lack of consensus in the structure of self-aggregation and its response to warming is a barrier to understanding. Gaining a deeper understanding of convective aggregation and tropical climate will require reducing the degrees of freedom with which convection can vary. Therefore, we propose phase II of RCEMIP (RCEMIP-II) that utilizes a prescribed sinusoidal sea surface temperature (SST) pattern to provide a constraint on the structure of convection and move one critical step up the model hierarchy. This so-called “mock-Walker” configuration generates features that resemble observed tropical circulations. The specification of the mock-Walker protocol for RCEMIP-II is described, along with example results from one CRM and one GCM. RCEMIP-II will consist of five required simulations: three simulations with the same three mean SSTs as in RCEMIP-I but with an SST gradient and two additional simulations at one of the mean SSTs with different values of the SST gradients. We also test the sensitivity to the imposed SST gradient and the domain size. Under weak SST gradients, unforced self-aggregation emerges across the entire domain, similar to what was found in RCEMIP. As the SST gradient increases, the convective region narrows and is more confined to the warmest SSTs. At warmer mean SSTs and stronger SST gradients, low-frequency variability in the convective aggregation emerges, suggesting that simulations of at least 200 d may be needed to achieve robust equilibrium statistics in this configuration. Simulations with different domain sizes generally have similar mean statistics and convective structures, depending on the value of the SST gradient. The prescribed SST boundary condition is the only difference in the set-up between RCEMIP-II and RCEMIP-I, which enables comparison between the two; however, we also welcome participation in RCEMIP-II from models that did not participate in RCEMIP-I. 

   more » « less
3. Identifying the Deep‐Inflow Mixing Features in Orographically‐Locked Diurnal Convection

   https://doi.org/10.1029/2023GL103107  

   Chang, Yu‐Hung; Chen, Wei‐Ting; Wu, Chien‐Ming; Kuo, Yi‐Hung; Neelin, J. David (May 2023, Geophysical Research Letters)

   Abstract Orographically‐locked diurnal convection involves interactions between local circulation and the thermodynamic environment of convection. Here, the relationships of convective updraft structures over orographic precipitation hotspots and their upstream environment in the TaiwanVVM large‐eddy simulations are analyzed for the occurrence of the orographic locking features. Strong convective updraft columns within heavily precipitating, organized systems exhibit a mass flux profile gradually increasing with height through a deep lower‐tropospheric inflow layer. Enhanced convective development is associated with higher upstream moist static energy (MSE) transport through this deep‐inflow layer via local circulation, augmenting the rain rate by 36% in precipitation hotspots. The simulations provide practical guidance for targeted observations within the most common deep‐inflow path. Preliminary field measurements support the presence of high MSE transport within the deep‐inflow layer when organized convection occurs at the hotspot. Orographically‐locked convection facilitate both modeling and field campaign design to examine the general properties of active deep convection. 

   more » « less
4. Challenges in Numerical Weather Prediction of the 10 August 2020 Midwestern Derecho: Examples from the FV3-LAM

   https://doi.org/10.1175/WAF-D-23-0019.1  

   Gallus, William A.; Harrold, Michell A. (June 2023, Weather and Forecasting)

   Abstract A severe derecho impacted the Midwestern United States on 10 August 2020, causing over 12 billion dollars in damage, and producing peak winds estimated at 63 m s −1 , with the worst impacts in Iowa. The event was not forecast well by operational forecasters, nor even by operational and quasi-operational convection-allowing models. In the present study, nine simulations are performed using the Limited Area Model version of the Finite-Volume-Cubed-Sphere model (FV3-LAM) with three horizontal grid spacings and two physics suites. In addition, when a prototype of the Rapid Refresh Forecast System (RRFS) physics is used, sensitivity tests are performed to examine the impact of using the Grell-Freitas (GF) convective scheme. Several unusual results are obtained. With both the RRFS (not using GF) and Global Forecast System (GFS) physics suites, simulations using relatively coarse 13 and 25 km horizontal grid spacing do a much better job of showing an organized convective system in Iowa during the daylight hours of 10 August than the 3-km grid spacing runs. In addition, the RRFS run with 25-km grid spacing becomes much worse when the GF convective scheme is used. The 3-km RRFS run that does not use the GF scheme develops spurious nocturnal convection the night before the derecho, removing instability and preventing the derecho from being simulated at all. When GF is used, the spurious storms are removed and an excellent forecast is obtained with an intense bowing echo, exceptionally strong cold pool, and roughly 50 m s −1 surface wind gusts. 

   more » « less
5. An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

   https://doi.org/10.1029/2022MS003508  

   Han, Yilun; Zhang, Guang J; Wang, Yong (October 2023, Journal of Advances in Modeling Earth Systems)

   Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020,https://doi.org/10.1029/2020MS002076) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling. 

   more » « less