Rotating radiative-convective equilibrium simulated by a cloud-resolving model

The results of a series of cloud-resolving radiative-convective equilibrium (RCE) simulations are presented. The RCE simulations, used as an idealization for the mean tropical climate, are run for a wide range of prescribed sea-surface temperatures (SSTs), from 21[superscript o]C to 36[superscript o]C, representing the range of past, present, and, possibly, future mean tropical SSTs. The RCE with constant Coriolis parameter f is contrasted with nonrotating RCE. The Coriolis parameter is artificially increased from typical values in the Tropics by about one order of magnitude to allow multiple tropical cyclones (TCs) to coexist in a relatively small 2300 × 2300 km[superscript 2] domain with a 3 km horizontal grid spacing. Nonrotating RCE is also simulated, but using a substantially smaller, 384 × 384 km[superscript 2] domain. Rotating RCE, which we nickname “TC World,” contains from 8 to 26 TCs with the average number of TCs monotonically decreasing with increasing SST. At the same time, the TCs' size, intensity, and per-TC precipitation rate tend to increase in response to increasing SST. For example, the average per-TC kinetic energy and precipitation rate tend to double for every 6[superscript o]C SST increase. These results are consistent with scaling laws in which TC velocities and inner core diameters scale with the potential intensity and its ratio to the Coriolis parameter, respectively, while the separation between cyclone centers appears to scale with the deformation radius. It is also found that the outflow temperature of TC's, as defined as the height of the local maximum of the upper-troposphere cloud fraction, remains relatively invariant with SST. The cold-point tropopause height in TC World is found to be about 2 km higher than the corresponding height in nonrotating RCE.National Science Foundation (U.S.) (Grant AGS1032244

Large-eddy simulation of stratocumulus-topped boundary layer with an explicit and a new bulk microphysics scheme.

The realism of the model is evaluated by a direct comparison of the model predictions with the aircraft observations of the STBL. The first case study is based on the UKMRF flight 526 measurements collected over the North Sea on 22 July 1982; the second case study corresponds to the ASTEX flight A209 flown on 12-13 June 1992. The model is able to reproduce reasonably well most of the observed boundary layer parameters, including turbulent fluxes and variances of various fields, intensity and vertical distribution of the turbulent kinetic energy, upward and downward radiation fluxes, and the cloud drop spectra.I designed a new bulk microphysical parameterization using the explicit model as a benchmark for comparison. The liquid water is divided into two categories--non-precipitable cloud water and drizzle, similar to traditional Kessler-type parameterizations. The water content and drop concentration are predicted for each category. The source/sink terms such as autoconversion of cloud water into drizzle are deduced directly from the drop size spectra predicted by the explicit microphysical model. The predictions of the LES model using the new bulk microphysics are compared with the predictions using explicit microphysics for two cases: non-drizzling and heavy-drizzling STBL. The results show that the new bulk microphysical model satisfactorily reproduces many characteristics of the STBL as simulated by explicit microphysical model.A case of stratocumulus-to-cumulus transition triggered by the depletion of CCN is simulated. It is shown that the response of the STBL to the increase in drizzle due to CCN depletion is the reduction of its cloud fractional cover and change of the character of circulation toward the cumulus convection. The boundary layer after the Sc-to-Cu transition consists of two layers: the well-mixed cloud free surface layer driven by surface heat fluxes and shear, and the conditionally unstable upper layer capped by the inversion with embedded cumulus clouds connected to the moisture and CCN supply in the surface layer.A new LES dynamical framework coupled with an explicit microphysical module has been developed. It is verified against analytical solution (linear mountain wave test) and against predictions from the other LES models. The results of the tests of the microphysical module convincingly show that the drop spectrum resolution in our model is adequate to accurately predict the cloud microphysics parameters

Global Cloud-Resolving Models

Global cloud-resolving models (GCRMs) are a new category of atmospheric global models designed to solve different flavors of the nonhydrostatic equations through the use of kilometer-scale global meshes. GCRMs make it possible to explicitly simulate deep convection, thereby avoiding the need for cumulus parameterization and allowing for clouds to be resolved by microphysical models responding to grid-scale forcing. GCRMs require high-resolution discretization over the globe, for which a variety of mesh structures have been proposed and employed. The first GCRM was constructed 15 years ago, and in recent years, other groups have also begun adopting this approach, enabling the first intercomparison studies of such models. Because conventional general circulation models (GCMs) suffer from large biases associated with cumulus parameterization, GCRMs are attractive tools for researchers studying global weather and climate. In this review, GCRMs are described, with some emphasis on their historical development and the associated literature documenting their use. The advantages of GCRMs are presented, and currently existing GCRMs are listed and described. Future prospects for GCRMs are also presented in the final section

Monsoon Intraseasonal Oscillations as simulated by the Superparameterized Community Atmosphere Model

The relative success of the Community Atmosphere Model with superparameterized convection (SP-CAM) in simulating the space-time characteristics of the Madden Julian Oscillation encourages us to examine its simulation of the Indian summer monsoon and monsoon intraseasonal oscillations (MISOs). While the model simulates the onset and withdrawal of the Indian monsoon realistically, it has a significant wet bias in boreal summer precipitation over the Asian monsoon region. The space-time characteristics of the MISOs simulated by the SP-CAM are examined in detail and compared with those of the observed MISO to gain insight into the model's bias in simulating the seasonal mean. During northern summer, the model simulates a 20 day mode and a 60 day mode in place of the observed 15 and 45 day modes, respectively. The simulated 20 day mode appears to have no observed analog with a baroclinic vertical structure and strong northward propagation over Indian longitudes. The simulated 60 day mode seems to be a lower-frequency version of the observed 45 day mode with relatively slower northward propagation. The model's underestimation of light rain events and overestimation of heavy rain events are shown to be responsible for the wet bias of the model. More frequent occurrence of heavy rain events in the model is, in turn, related to the vertical structure of the higher-frequency modes. Northward propagation of the simulated 20 day mode is associated with a strong cyclonic vorticity at low levels north of the heating maximum associated with a smaller meridional scale of the simulated mode. The simulated vertical structure of heating indicates a strong maximum in the upper troposphere between 200 and 300 hPa. Such a heating profile seems to generate a higher-order baroclinic mode response with smaller meridional structure, stronger low-level cyclonic vorticity, enhanced low-level moisture convergence, and higher precipitation. Therefore, the vertical structure of heating simulated by the cloud-resolving model within SP-CAM may hold the key for improving the precipitation bias in the model

Simulation of the intraseasonal variability over the Eastern Pacific ITCZ in climate models

During boreal summer, convective activity over the eastern Pacific (EPAC) inter-tropical convergence zone (ITCZ) exhibits vigorous intraseasonal variability (ISV). Previous observational studies identified two dominant ISV modes over the EPAC, i.e., a 40-day mode and a quasi-biweekly mode (QBM). The 40-day ISV mode is generally considered a local expression of the Madden-Julian Oscillation. However, in addition to the eastward propagation, northward propagation of the 40-day mode is also evident. The QBM mode bears a smaller spatial scale than the 40-day mode, and is largely characterized by northward propagation. While the ISV over the EPAC exerts significant influences on regional climate/weather systems, investigation of contemporary model capabilities in representing these ISV modes over the EPAC is limited. In this study, the model fidelity in representing these two dominant ISV modes over the EPAC is assessed by analyzing six atmospheric and three coupled general circulation models (GCMs), including one super-parameterized GCM (SPCAM) and one recently developed high-resolution GCM (GFDL HIRAM) with horizontal resolution of about 50 km. While it remains challenging for GCMs to faithfully represent these two ISV modes including their amplitude, evolution patterns, and periodicities, encouraging simulations are also noted. In general, SPCAM and HIRAM exhibit relatively superior skill in representing the two ISV modes over the EPAC. While the advantage of SPCAM is achieved through explicit representation of the cumulus process by the embedded 2-D cloud resolving models, the improved representation in HIRAM could be ascribed to the employment of a strongly entraining plume cumulus scheme, which inhibits the deep convection, and thus effectively enhances the stratiform rainfall. The sensitivity tests based on HIRAM also suggest that fine horizontal resolution could also be conducive to realistically capture the ISV over the EPAC, particularly for the QBM mode. Further analysis illustrates that the observed 40-day ISV mode over the EPAC is closely linked to the eastward propagating ISV signals from the Indian Ocean/Western Pacific, which is in agreement with the general impression that the 40-day ISV mode over the EPAC could be a local expression of the global Madden-Julian Oscillation (MJO). In contrast, the convective signals associated with the 40-day mode over the EPAC in most of the GCM simulations tend to originate between 150°E and 150°W, suggesting the 40-day ISV mode over the EPAC might be sustained without the forcing by the eastward propagating MJO. Further investigation is warranted towards improved understanding of the origin of the ISV over the EPAC

DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains

A review of the experimental protocol and motivation for DYAMOND, the first intercomparison project of global storm-resolving models, is presented. Nine models submitted simulation output for a 40-day (1 August–10 September 2016) intercomparison period. Eight of these employed a tiling of the sphere that was uniformly less than 5 km. By resolving the transient dynamics of convective storms in the tropics, global storm-resolving models remove the need to parameterize tropical deep convection, providing a fundamentally more sound representation of the climate system and a more natural link to commensurately high-resolution data from satellite-borne sensors. The models and some basic characteristics of their output are described in more detail, as is the availability and planned use of this output for future scientific study. Tropically and zonally averaged energy budgets, precipitable water distributions, and precipitation from the model ensemble are evaluated, as is their representation of tropical cyclones and the predictability of column water vapor, the latter being important for tropical weather

Clouds and convective self-aggregation in a multi-model ensemble of radiative-convective equilibrium simulations

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society

Use of ARM Data to address the Climate Change Further Development and Applications of A Multi-scale Modeling Framework

The Colorado State University (CSU) Multi-scale Modeling Framework (MMF) is a new type of general circulation model (GCM) that replaces the conventional parameterizations of convection, clouds and boundary layer with a cloud-resolving model (CRM) embedded into each grid column. The MMF that we have been working with is a “super-parameterized” version of the Community Atmosphere Model (CAM). As reported in the publications listed below, we have done extensive work with the model. We have explored the MMF’s performance in several studies, including an AMIP run and a CAPT test, and we have applied the MMF to an analysis of climate sensitivity