Global Cloud-resolving Model Research Articles

Climate of High-obliquity Exoterrestrial Planets with a Three-dimensional Cloud System Resolving Climate Model

Planetary climates are strongly affected by planetary orbital parameters such as obliquity, eccentricity, and precession. In exoplanetary systems, exoterrestrial planets should have various obliquities. High-obliquity planets would have extreme seasonal cycles due to the seasonal change of the distribution of the insolation. Here, we introduce the Non-hydrostatic ICosahedral Atmospheric Model (NICAM), a global cloud-resolving model, to investigate the climate of high-obliquity planets. This model can explicitly simulate a three-dimensional cloud distribution and vertical transports of water vapor. We simulated exoterrestrial climates with high resolution using the supercomputer FUGAKU. We assumed aqua-planet configurations with 1 bar of air as a background atmosphere, with four different obliquities (0°, 23.5°, 45°, and 60°). We ran two sets of simulations: (1) low resolution (∼220 km mesh as the standard resolution of a general circulation model for exoplanetary science) with parameterization for cloud formation, and (2) high resolution (∼14 km mesh) with an explicit cloud microphysics scheme. Results suggest that high-resolution simulations with an explicit treatment of cloud microphysics reveal warmer climates due to less low cloud fraction and a large amount of water vapor in the atmosphere. It implies that treatments of cloud-related processes lead to a difference between different resolutions in climatic regimes in cases with high obliquities.

On the Semidiurnal Variation in Surface Rainfall Rate over the Tropics in a Global Cloud-Resolving Model Simulation and Satellite Observations

On the Semidiurnal Variation in Surface Rainfall Rate over the Tropics in a Global Cloud-Resolving Model Simulation and Satellite Observations

Improving Representation of Tropical Cloud Overlap in GCMs Based on Cloud-Resolving Model Data

The decorrelation length (Lcf) has been widely used to describe the behavior of vertical overlap of clouds in general circulation models (GCMs); however, it has been a challenge to associate Lcf with the large-scale meteorological conditions during cloud evolution. This study explored the relationship between Lcf and the strength of atmospheric convection in the tropics based on output from a global cloud-resolving model. Lcf tends to increase with vertical velocity in the mid-troposphere (w500) at locations of ascent, but shows little or no dependency on w500 at locations of descent. A representation of Lcf as a function of vertical velocity is obtained, with a linear regression in ascending regions and a constant value in descending regions. This simple and dynamic-related representation of Lcf leads to a significant improvement in simulation of both cloud cover and radiation fields compared with traditional overlap treatments. This work presents a physically justifiable approach to depicting cloud overlap in the tropics in GCMs.

Insights into low-latitude cloud feedbacks from high-resolution models.

Cloud feedbacks are a leading source of uncertainty in the climate sensitivity simulated by global climate models (GCMs). Low-latitude boundary-layer and cumulus cloud regimes are particularly problematic, because they are sustained by tight interactions between clouds and unresolved turbulent circulations. Turbulence-resolving models better simulate such cloud regimes and support the GCM consensus that they contribute to positive global cloud feedbacks. Large-eddy simulations using sub-100 m grid spacings over small computational domains elucidate marine boundary-layer cloud response to greenhouse warming. Four observationally supported mechanisms contribute: 'thermodynamic' cloudiness reduction from warming of the atmosphere-ocean column, 'radiative' cloudiness reduction from CO2- and H2O-induced increase in atmospheric emissivity aloft, 'stability-induced' cloud increase from increased lower tropospheric stratification, and 'dynamical' cloudiness increase from reduced subsidence. The cloudiness reduction mechanisms typically dominate, giving positive shortwave cloud feedback. Cloud-resolving models with horizontal grid spacings of a few kilometres illuminate how cumulonimbus cloud systems affect climate feedbacks. Limited-area simulations and superparameterized GCMs show upward shift and slight reduction of cloud cover in a warmer climate, implying positive cloud feedbacks. A global cloud-resolving model suggests tropical cirrus increases in a warmer climate, producing positive longwave cloud feedback, but results are sensitive to subgrid turbulence and ice microphysics schemes.

Performance assessment of a triple-frequency spaceborne cloud–precipitation radar concept using a global cloud-resolving model

Abstract. Multi-frequency radars offer enhanced detection of clouds and precipitation compared to single-frequency systems, and are able to make more accurate retrievals when several frequencies are available simultaneously. An evaluation of a spaceborne three-frequency Ku-/Ka-/W-band radar system is presented in this study, based on modeling radar reflectivities from the results of a global cloud-resolving model with a 875 m grid spacing. To produce the reflectivities, a scattering model has been developed for each of the hydrometeor types produced by the model, as well as for melting snow. The effects of attenuation and multiple scattering on the radar signal are modeled using a radiative transfer model, while nonuniform beam filling is reproduced with spatial averaging. The combined effects of these are then quantified both globally and in six localized case studies. Two different orbital scenarios using the same radar are compared. Overall, based on the results, it is expected that the proposed radar would detect a high-quality signal in most clouds and precipitation. The main exceptions are the thinnest clouds that are below the detection threshold of the W-band channel, and at the opposite end of the scale, heavy convective rainfall where a combination of attenuation, multiple scattering and nonuniform beam filling commonly cause significant deterioration of the signal; thus, while the latter can be generally detected, the quality of the retrievals is likely to be degraded.

Convective Momentum Transport Associated with the Madden–Julian Oscillation Based on a Reanalysis Dataset

Abstract A better understanding of multiscale interactions within the Madden–Julian oscillation (MJO), including momentum exchanges, is critical for improved MJO prediction skill. In this study, convective momentum transport (CMT) associated with the MJO is analyzed based on the NOAA Climate Forecast System Reanalysis (CFSR). A three-layer vertical structure associated with the MJO, as previously suggested in the mesoscale momentum tendency profile based on global cloud-resolving model simulations, is evident in the subgrid-scale momentum tendency from the CFSR. Positive (negative) subgrid-scale momentum tendency anomalies are found near the surface, negative (positive) anomalies are found in the low to midtroposphere, and positive (negative) anomalies in the upper troposphere are found within and to the west (east) of the MJO convection. This tends to damp the MJO circulation in the free atmosphere, while enhancing MJO winds near the surface. In addition, it could also reduce the MJO eastward propagation speed and lead to the backward tilt with height in the observed MJO structure through a secondary circulation near the MJO center. Further analyses illustrate that this three-layer vertical structure in subgrid-scale momentum tendency largely balances the grid-scale momentum transport of the zonal wind component u, mainly through the transport of seasonal mean u by the MJO-scale vertical motion. Synoptic-scale systems, which were previously proposed to be essential for the u-momentum transport of the MJO, however, are found to play a minor role for the total grid-scale momentum tendency. The above momentum tendency structure is also confirmed with the ECMWF analysis for the Year of Tropical Convection (YOTC) that lends confidence to these above results based on the CFSR.

A global climate model agent for high spatial and temporal resolution data

Fine cell granularity in modern climate models can produce terabytes of data in each snapshot, causing significant I/O overhead. To address this issue, a method of reducing the I/O latency of high-resolution climate models by identifying and selectively outputting regions of interest is presented. Working with a global cloud-resolving model and running with up to 10,240 processors on a Cray XE6, this method provides significant I/O bandwidth reduction depending on the frequency of writes and the size of the region of interest. The implementation challenges of determining global parameters in a strictly core-localized model and properly formatting output files that only contain subsections of the global grid are addressed, as well as the overall bandwidth impact and benefits of the method. The gains in I/O throughput provided by this method allow dual output rates for high-resolution climate models: a low-frequency global snapshot as well as a high-frequency regional snapshot when events of particular interest occur.

Aerosol effect on the warm rain formation process: Satellite observations and modeling

This study demonstrates how aerosols influence the liquid precipitation formation process. This demonstration is provided by the combined use of satellite observations and global high‐resolution model simulations. Methodologies developed to examine the warm cloud microphysical processes are applied to both multi‐sensor satellite observations and aerosol‐coupled global cloud‐resolving model (GCRM) results to illustrate how the warm rain formation process is modulated under different aerosol conditions. The observational analysis exhibits process‐scale signatures of rain suppression due to increased aerosols, providing observational evidence of the aerosol influence on precipitation. By contrast, the corresponding statistics obtained from the model show a much faster rain formation even for polluted aerosol conditions and much weaker reduction of precipitation in response to aerosol increase. It is then shown that this reduced sensitivity points to a fundamental model bias in the warm rain formation process that in turn biases the influence of aerosol on precipitation. A method of improving the model bias is introduced in the context of a simplified single‐column model (SCM) that represents the cloud‐to‐rain water conversion process in a manner similar to the original GCRM. Sensitivity experiments performed by modifying the model assumptions in the SCM and their comparisons to satellite statistics both suggest that the auto‐conversion scheme has a critical role in determining the precipitation response to aerosol perturbations and also provide a novel way of constraining key parameters in the auto‐conversion schemes of global models.

Efficient data IO for a Parallel Global Cloud Resolving Model

Execution of a Global Cloud Resolving Model (GCRM) at target resolutions of 2–4 km will generate, at a minimum, 10s of Gigabytes of data per variable per snapshot. Writing this data to disk, without creating a serious bottleneck in the execution of the GCRM code, while also supporting efficient post-execution data analysis is a significant challenge. This paper discusses an Input/Output (IO) application programmer interface (API) for the GCRM that efficiently moves data from the model to disk while maintaining support for community standard formats, avoiding the creation of very large numbers of files, and supporting efficient analysis. Several aspects of the API will be discussed in detail. First, we discuss the output data layout which linearizes the data in a consistent way that is independent of the number of processors used to run the simulation and provides a convenient format for subsequent analyses of the data. Second, we discuss the flexible API interface that enables modelers to easily add variables to the output stream by specifying where in the GCRM code these variables are located and to flexibly configure the choice of outputs and distribution of data across files. The flexibility of the API is designed to allow model developers to add new data fields to the output as the model develops and new physics is added. It also provides a mechanism for allowing users of the GCRM code to adjust the output frequency and the number of fields written depending on the needs of individual calculations. Third, we describe the mapping to the NetCDF data model with an emphasis on the grid description. Fourth, we describe our messaging algorithms and IO aggregation strategies that are used to achieve high bandwidth while simultaneously writing concurrently from many processors to shared files. We conclude with initial performance results.

Aerosol indirect effects in a multi-scale aerosol-climate model PNNL-MMF

Abstract. Much of the large uncertainty in estimates of anthropogenic aerosol effects on climate arises from the multi-scale nature of the interactions between aerosols, clouds and dynamics, which are difficult to represent in conventional general circulation models (GCMs). In this study, we use a multi-scale aerosol-climate model that treats aerosols and clouds across multiple scales to study aerosol indirect effects. This multi-scale aerosol-climate model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each vertical column of a GCM grid. The extension allows a more physically-based treatment of aerosol-cloud interactions in both stratiform and convective clouds on the global scale in a computationally feasible way. Simulated model fields, including liquid water path (LWP), ice water path, cloud fraction, shortwave and longwave cloud forcing, precipitation, water vapor, and cloud droplet number concentration are in reasonable agreement with observations. The new model performs quantitatively similar to the previous version of the MMF model in terms of simulated cloud fraction and precipitation. The simulated change in shortwave cloud forcing from anthropogenic aerosols is −0.77 W m−2, which is less than half of that (−1.79 W m−2) calculated by the host GCM (NCAR CAM5) with traditional cloud parameterizations and is also at the low end of the estimates of other conventional global aerosol-climate models. The smaller forcing in the MMF model is attributed to a smaller (3.9 %) increase in LWP from preindustrial conditions (PI) to present day (PD) compared with 15.6 % increase in LWP in stratiform clouds in CAM5. The difference is caused by a much smaller response in LWP to a given perturbation in cloud condensation nuclei (CCN) concentrations from PI to PD in the MMF (about one-third of that in CAM5), and, to a lesser extent, by a smaller relative increase in CCN concentrations from PI to PD in the MMF (about 26 % smaller than that in CAM5). The smaller relative increase in CCN concentrations in the MMF is caused in part by a smaller increase in aerosol lifetime from PI to PD in the MMF, a positive feedback in aerosol indirect effects induced by cloud lifetime effects from aerosols. The smaller response in LWP to anthropogenic aerosols in the MMF model is consistent with observations and with high resolution model studies, which may indicate that aerosol indirect effects simulated in conventional global climate models are overestimated and point to the need to use global high resolution models, such as MMF models or global CRMs, to study aerosol indirect effects. The simulated total anthropogenic aerosol effect in the MMF is −1.05 W m−2, which is close to the Murphy et al. (2009) inverse estimate of −1.1±0.4 W m−2 (1σ) based on the examination of the Earth's energy balance. Further improvements in the representation of ice nucleation and low clouds in MMF are needed to refine the aerosol indirect effect estimate.

Development of a Quasi‐3D Multiscale Modeling Framework: Motivation, Basic Algorithm and Preliminary results

A new framework for modeling the atmosphere, which we call the quasi‐3D (Q3D) multi‐scale modeling framework (MMF), is developed with the objective of including cloud‐scale three‐dimensional effects in a GCM without necessarily using a global cloud‐resolving model (CRM). It combines a GCM with a Q3D CRM that has the horizontal domain consisting of two perpendicular sets of channels, each of which contains a locally 3D grid‐point array. For computing efficiency, the widths of the channels are chosen to be narrow. Thus, it is crucial to select a proper lateral boundary condition to realistically simulate the statistics of cloud and cloud‐associated processes. Among the various possibilities, a periodic lateral boundary condition is chosen for the deviations from background fields that are obtained by interpolations from the GCM grid points. Since the deviations tend to vanish as the GCM grid size approaches that of the CRM, the whole system of the Q3D MMF can converge to a fully 3D global CRM. Consequently, the horizontal resolution of the GCM can be freely chosen depending on the objective of application, without changing the formulation of model physics. To evaluate the newly developed Q3D CRM in an efficient way, idealized experiments have been performed using a small horizontal domain. In these tests, the Q3D CRM uses only one pair of perpendicular channels with only two grid points across each channel. Comparing the simulation results with those of a fully 3D CRM, it is concluded that the Q3D CRM can reproduce most of the important statistics of the 3D solutions, including the vertical distributions of cloud water and precipitants, vertical transports of potential temperature and water vapor, and the variances and covariances of dynamical variables. The main improvement from a corresponding 2D simulation appears in the surface fluxes and the vorticity transports that cause the mean wind to change. A comparison with a simulation using a coarse‐resolution 3D CRM is also made.

全球雲解像モデルによる気候変動予測

We have developed a new type of atmospheric general circulation model, “global cloud-resolving model” which covers the globe with a mesh size of a few kilometers. This model enables us to directly simulate the multi-scale structure of the cloud systems of the Earth from meso-scale to planetary scale cloud disturbances. In particular, tropical cloud systems including Madden-Julian Oscillation and tropical cyclogenesis are realistically simulated by the global cloud-resolving model. It is contrasted with current atmospheric general circulation models that are used for climate projections, because they cannot simulate realistic tropical cloud disturbances in general. The global cloud-resolving model not only reproduces the geographical distribution of cloud-precipitation systems and their diurnal to intra-seasonal variabilities, but also more physically reproduces cloud properties by explicitly implementing cloud microphysical processes. It is expected that the global cloud-resolving model will play a major role in climate simulations, and that future climate projections will be more reliable than current approaches.

Diurnal Cycle of Precipitation in the Tropics Simulated in a Global Cloud-Resolving Model

Abstract This study analyzes the diurnal cycle of precipitation simulated in a global cloud-resolving model (GCRM) named the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). A 30-day integration of NICAM successfully simulates the precipitation diurnal cycle associated with the land–sea breeze and the thermally induced topographic circulations as well as the horizontal propagation of diurnal cycle signals. The first harmonic of the diurnal cycle of precipitation in the 7-km run agrees well with that from satellite observations in its geographical distributions although its amplitude is slightly overestimated. The NICAM simulation revealed that the precipitation diurnal cycle over the Maritime Continent is strongly coupled with the land–sea breeze that controls the convergence/divergence pattern in the lower troposphere around the islands. The analysis also suggests that the cold pool often forms over the open ocean where the precipitation intensity is high, and the propagation of the cold pool events is related to the precipitation diurnal cycle as well as the land–sea breeze. Sensitivity experiments suggest a prominent horizontal resolution dependence of the simulated precipitation diurnal cycle. Over continental areas the 14-km run induces the diurnal peak about three hours later than the 7-km run. The 3.5-km run produces the peak time and amplitude that are very similar to those in Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations. Meanwhile, the resolution dependence in phase and amplitude is negligibly small over the open oceans. This contrast sensitivity to the horizontal resolution is attributed to the differences in structure and life cycle of convective systems over land and ocean. Diurnal peaks of precipitable water vapor, precipitation, and outgoing longwave radiation (OLR) are compared over land areas using the NICAM 7-km run. The daily precipitable water vapor maximum appears around 1500 local time (LT), which is followed by the precipitation peak around 1630 LT. The diurnal cycle of high clouds tends to peak around 1930 LT, three hours later than the precipitation peak. These results from NICAM simulations can explain the cause of the phase differences among precipitation products based on several satellite observations. The authors demonstrate that the GCRM is a promising tool for realistically simulating the precipitation diurnal cycle and could be quite useful for studying the role of the diurnal cycle in the climate systems in a global context.

Spontaneous onset of a Madden-Julian oscillation event in a cloud-system-resolving simulation

[1] Spontaneous onset of a Madden-Julian Oscillation (MJO) event in November 2006 was reproduced at a proper location and time by a global cloud-resolving model (CRM) used with a relatively coarse horizontal grid. Preconditioning of moisture was simulated about 4-days prior to the onset in the Indian Ocean, which agreed with data obtained in an in-situ observation. To investigate influence of zonal Sea Surface Temperature (SST) gradient in the Indian Ocean, we conducted a sensitivity study comparing composites made from five ensemble simulations. It was found that the eastward-moving signal of this MJO event could be obscured if SST were zonally uniform in the western Indian Ocean. Zonal SST gradient has not been considered important in the previous studies about the MJO onset, but SST distribution locating cooler SST in the west side possibly help enhance convection in slow eastward-moving envelopes of the MJO.

Importance of the subgrid-scale turbulent moist process: Cloud distribution in global cloud-resolving simulations

This study investigated the turbulent transport process within the nonhydrostatic icosahedral atmospheric model (NICAM), a global cloud-resolving model (GCRM), with particular focus on the spatial reproducibility of cloud characteristics in NICAM simulations. A turbulent closure model was applied, based on level 2 of the model developed by Nakanishi and Niino ([Nakanishi, M., and Niino, H., 2006: An improved Mellor–Yamada level-3 model: its numerical stability and application to a regional prediction of advection fog. Boundary-Layer Meteorol., 119, 397–407.]; a revised version of the original Mellor–Yamada model), together with a subgrid-scale condensation process. NICAM simulations were conducted for boreal summer of 2004 using mesh sizes of about 14 km and 7 km. Simulated cloud amounts and radiative budget were compared with observed data. The results confirmed an improvement in the spatial distribution of low clouds that develop in offshore regions of subtropical continents compared to past NICAM experiments ([Iga, S., Tomita, H., Tsushima, Y., and Satoh, M., 2007: Climatology of a nonhydrostatic global model with explicit cloud process. Geophys. Res. Lett., 34, L22814, doi:10.1029/2007/GL031048.]). A sensitivity study of subgrid-scale clouds in the turbulent closure scheme revealed that the turbulent transport process modulated by the subgrid-scale cloud strongly controls not only the low-cloud amount but also mid- and high-cloud amounts. This indicates that parameterization of unresolvable subgrid-scale clouds remains an important component of cloud behavior in GCRMs.

Relationship between radar reflectivity and the time scale of warm rain formation in a global cloud-resolving model

The connections between radar reflectivity and the time scale of warm rain formation are examined within a global cloud-resolving model. The parameterizations formulae of auto-conversion and accretion processes in the model reveal specific relationships between the time scale for auto-conversion and radar reflectivity of cloud water as well as between the time scale for accretion and radar reflectivity of rain water. The overall time scale for warm rain formation, determined by combined contributions from these processes, is found to relate with total radar reflectivity in the manner that varies with cloud-rain composition between auto-conversion and accretion limits. The global statistics from the model output reveals that the time scale is closely related with the total radar reflectivity, thus suggesting that the radar reflectivity is a gross measure of the warm rain time scale. The relationship developed is applied to both model-simulated and CloudSat-observed radar reflectivities to compare the time scales of warm rain formation between observation and the model. Comparison of the time scale so derived reveals significant differences between the model and CloudSat observations. These differences suggest that the simulated cloud-rain composition in the model is biased toward larger rain water contents and smaller content of cloud water compared to reality due to an accelerated cloud-to-rain water conversion in the model.

A Study of Aerosol Indirect Effect with a Global Cloud-Resolving Model

A Study of Aerosol Indirect Effect with a Global Cloud-Resolving Model

A PDF-based hybrid prognostic cloud scheme for general circulation models

A new cloud parameterization based on prognostic equations for the subgrid-scale fluctuations in temperature and total water content is introduced for global climate models. The proposed scheme, called hybrid prognostic cloud (HPC) parameterization, employs simple probability density functions (PDFs) to the horizontal subgrid-scale inhomogeneity, allowing them to vary in shape in response to small-scale processes such as cumulus detrainment and turbulent mixing. Simple tests indicate that the HPC scheme is highly favorable as compared to a diagnostic scheme in terms of the cloud fraction and cloud water content under either uniform or non-uniform forcing. The relevance of the HPC scheme is investigated by implementing it in an atmospheric component model of the climate model MIROC with a coarse resolution of T42. A comparison of the short-term integrations between the T42 model and a global cloud resolving model (GCRM) reveals that the HPC scheme can reproduce, to a certain degree, the subgrid-scale variance and skewness of temperature and total water content simulated in the GCRM. It is also found that the HPC scheme significantly alters the climatological distributions in cloud cover, precipitation, and moisture, which are all improved from the model using a conventional diagnostic cloud scheme.

A joint satellite and global cloud‐resolving model analysis of a Madden‐Julian Oscillation event: Model diagnosis

In this study, a methodology to diagnose a global cloud‐resolving model (GCRM) is explored on the basis of a joint analysis with satellite measurements. The Madden‐Julian Oscillation experiment carried out with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) is used as the test bed. The NICAM output is compared with Tropical Rainfall Measuring Mission (TRMM) and CloudSat measurements in terms of composite analysis, contoured frequency by altitude diagrams (CFADs), and the joint histogram of cloud top and precipitation top heights. It is found in the composite diagram that the GCRM reproduces a slow, eastward migration of a convective envelope well comparable to the satellite measurement. The GCRM CFAD qualitatively reproduces TRMM and CloudSat CFADs, except that the GCRM tends to overly produce snow in deep convection. The joint histograms reveal that, while the overproduction of snow is evident, NICAM‐simulated snow is incapable of producing 94‐GHz radar echoes higher than 5 dBZ. This deficiency can be mitigated by a modification to microphysical parameterization in the way that a proportion of small particles is enhanced in the snow mass spectrum.

Coarse-Resolution Models Only Partly Cloudy

In their report, “A Madden-Julian Oscillation event realistically simulated by a global cloud-resolving model” (14 December 2007, p. [1763][1]), H. Miura et al. promote the impression that coarse-resolution climate models cannot simulate the Madden-Julian Oscillation (MJO). We would like to