Table 3
Source publication
CGILS – the CFMIP-GASS Intercomparison of Large Eddy Models (LESs) and Single Column Models (SCMs) – investigates the mechanisms of cloud feedback in SCMs and LESs under idealized climate change perturbation. This paper describes the CGILS results from 15 SCMs and eight LES models. Three cloud regimes over the subtropical oceans are studied: shallo...
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Context 1
... schemes can differ in their entrainment and detrainment rates, the closure that determines the amount of cloud base mass flux, and convection triggering condition as well as origination level of convection. Table 3 categorizes the convective schemes in the SCMs based on these main attributes. Among the SCMs, CLUBB and RACMO use a single scheme to parameterize PBL turbulence and shallow convection. ...
Context 2
... shown in Zhang et al. [2012] for the GFDL model, this unrealistic behavior is related to the use of steady forcing. When compared with the LES results of Tables 3-5 in Blossey et al. [2013], the SCM surface latent heat fluxes are generally smaller than in the LES models. This is likely related to the use of the steady forcing or insufficient entrainment mixing in the SCMs. ...
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Citations
... Marine stratocumulus (Sc) clouds have the most extensive coverage over the Earth's surface compared to other cloud types (Hahn & Warren, 2007;Wood, 2012), significantly influencing the climate system. They play a crucial role in climate prediction uncertainties (Cess et al., 1990;Soden & Vecchi, 2011;Vial et al., 2013;H. Zhang et al., 2018;M. Zhang et al., 2013). Sc clouds are controlled by large-scale meteorological factors (MFs), such as inversion strength (measured by the estimated inversion strength or EIS; Wood & Bretherton, 2006), sea surface temperature (SST), low-level temperature advection (T adv ), large-scale vertical velocity (ω), free-tropospheric (FT) moisture, and surface wind sp ...
Plain Language Summary
Stratocumulus clouds have extensive coverage over the oceans and modulate the climate system by efficiently reflecting incoming solar radiation back to space. However, their simulations in climate models are challenging due to complex meteorological controls, in which temperature advection is one of the most uncertain controlling factors. To enhance our understanding, we examine the stratocumulus evolution influenced by cold‐advection (CADV) and warm‐advection (WADV) in the midlatitudes in a climate model, CESM2. A too rapid decrease in low‐cloud fraction (LCF) and cloud liquid water path (CLWP) is erroneously simulated under CADV conditions, while an increase in CLWP is substantially overestimated under WADV conditions. Using an explainable machine learning approach, these errors are found to be caused by the amplified drying or moistening effects due to improper treatments of meteorological controls on clouds in CESM2. This study suggests that these misrepresentations of cloud physics in the midlatitudes should be imperatively improved to reduce climate prediction uncertainties.
... This could lead to a reduction in large-scale cloud fraction in a GCM in response to decoupling. For example, Zhang et al. (2013) argued that positive cloud feedbacks were caused by enhanced turbulent cloud top entrainment in some single column models (SCMs) run as part of the CFMIP-GASS Intercomparison of Large Eddy Simulations and SCMs (CGILS, Blossey et al., 2013;Zhang et al., 2013). ...
... This could lead to a reduction in large-scale cloud fraction in a GCM in response to decoupling. For example, Zhang et al. (2013) argued that positive cloud feedbacks were caused by enhanced turbulent cloud top entrainment in some single column models (SCMs) run as part of the CFMIP-GASS Intercomparison of Large Eddy Simulations and SCMs (CGILS, Blossey et al., 2013;Zhang et al., 2013). ...
... This mechanism is very similar to second stage of the mechanism proposed by Wyant et al. (1997), albeit starting from a cumulus boundary layer rather than a well mixed stratocumulus boundary layer, and set in the context of climate warming rather than the stratocumulus to trade cumulus transition. Subsequently Zhang et al. (2013) examined positive shallow cloud feedbacks in the CGILS SCMs in cases where the shallow convection schemes were active and made the related argument that active convection could cause larger ventilation of the cloud layer in a warmer climate, leading to a decrease in cloud and a positive cloud feedback. ...
We investigate positive subtropical low cloud feedback mechanisms in climate models which have performed the CMIP6/CFMIP‐3 AMIP and AMIP uniform +4K experiments while saving CFMIP‐3 process diagnostics on model levels. Our analysis focuses on the trade cumulus/stratocumulus transition region between California and Hawaii, where positive low cloud feedbacks are present in the JJA season. We introduce a methodology to test various positive cloud feedback mechanisms proposed in the literature as the main causes of the low cloud responses in the models. Causal hypotheses are tested by comparing their predictions with the models' responses of clouds, cloud controlling factors, boundary layer depth and temperature/humidity tendencies to climate warming. Changes in boundary layer depth, relative humidity in the cloud layer, convective moistening rate and large‐scale humidity advection at the top of the boundary layer are shown to be crucial for identifying the main causes of the low cloud reductions in the models. For the cases examined, our approach narrows down the seven mechanisms considered to between one and three remaining candidates for each model. No single mechanism considered can explain the feedback in all of the models at the locations examined, but the surface latent heat flux/convective entrainment mechanism remains a candidate for BCC‐CSM2‐MR, IPSL‐CM6A‐LR, and MRI‐ESM2.0, while the surface upwelling longwave mechanism remains for CESM2, HadGEM3‐GC3.1‐LL, and MIROC6.
... This yields 1,536 grid points on a horizontal side, and 175 vertical grid levels. The grid's size limits our simulations' integration time to 60 hr, which is substantially shorter than the 20 days that for example, M. Zhang et al. (2013) run their small-domain simulations to come into equilibrium with their larger-scale environment. Our simulations do not reach such an equilibrium. ...
Small shallow cumulus clouds (<1 km) over the tropical oceans appear to possess the ability to self‐organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self‐organized convection, we present Cloud Botany, an ensemble of 103 large‐eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger‐scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC⁴A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger‐scale environment and patterns in mesoscale, self‐organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross‐wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross‐sections, as well as post‐processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time‐series). The data set is directly accessible from Python through the use of the EUREC⁴A intake catalog.
... For instance, the magnitude of the increase in specific humidity is larger at lower altitudes, which enhances the moisture contrast between the free troposphere and the boundary layer (Figure 3g, blue). As a result, the upward moisture flux by shallow convection increases, which tends to decrease the low cloud (Figures S5c and S5f in Supporting Information S1, red, Zhang et al., 2013;. In contrast, we also note that the vertical temperature profile stabilizes with warming, which increases strength of the inversion capping the boundary layer (Figure 3h, blue). ...
Plain Language Summary
We project future climate change induced by atmospheric greenhouse gas increases by conducting numerical simulations using specialized computer codes, namely Global Climate Models. Results of such simulations are characterized by decreases in low cloud with warming at the Earth's surface, which amplifies the warming by reflecting less sunlight back to space and allowing more sunlight to be absorbed at the surface. This amplifying effect, called “positive low cloud feedback,” is important because the amount of future warming affects our living and safety. However, the mechanism of the low cloud decreases with warming is not well understood. Here we propose that the low cloud decrease is primarily caused by increase in upward longwave radiation from the sea surface. We devise numerical simulations that enable the separation of the low cloud feedback into components coming from physically distinct causes. Results of the simulations indicate that increases in upward longwave radiation from the sea surface cause warming and drying near the Earth's surface, leading to the low cloud decrease. This mechanism is different from previously proposed understanding that the low cloud decrease is due to increases in sea surface evaporation or vertical moisture contrast.
... We do not have quantitative theories of their response to climate change (Bretherton, 2015), and shortcomings in their representation in climate models have long dominated uncertainties in climate projections (Bony & Dufresne, 2005;Cess et al., 1990Cess et al., , 1996Dufresne & Bony, 2008;Vial et al., 2013;Webb et al., 2006Webb et al., , 2013Zelinka et al., 2017). Numerical experiments have been limited to studies that have explored a few dozen canonical situations, mostly in the tropics (Blossey et al., , 2016Caldwell & Bretherton, 2009;Rauber et al., 2007;Sandu & Stevens, 2011;Schalkwijk et al., 2015;Siebesma et al., 2003;Tan et al., 2016Tan et al., , 2017Zhang et al., 2012Zhang et al., , 2013. Broader exploration has been limited by the computational expense necessary to resolve the meter-scale dynamics of low clouds in large-eddy simulations (LES). ...
Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly understood and are inaccurately represented in climate models. A principal reason is that the high computational expense of simulating them with large‐eddy simulations (LES) has inhibited broad and systematic numerical experimentation and the generation of large data sets for training parametrization schemes for climate models. Here we demonstrate LES of low clouds on tensor processing units (TPUs), application‐specific integrated circuits that were originally developed for machine learning applications. We show that TPUs in conjunction with tailored software implementations can be used to simulate computationally challenging stratocumulus clouds in conditions observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) field study. The TPU‐based LES code successfully reproduces clouds during DYCOMS and opens up the large computational resources available on TPUs to cloud simulations. The code enables unprecedented weak and strong scaling of LES, making it possible, for example, to simulate stratocumulus with 10× speedup over real‐time evolution in domains with a 34.7 km × 53.8 km horizontal cross section. The results open up new avenues for computational experiments and for substantially enlarging the sample of LES available to train parameterizations of low clouds.
... Experiments using LES demonstrate that the difference in response may be explained by several counteracting processes Bretherton, 2015). These processes are of more or less importance in different regions and may be resolved or parameterized in different ways in GCMs Zhang et al., 2013;Vial et al., 2016). For example, Yamaguchi et al. (2017) showed that prognostic cloud droplet number concentrations (CDNCs), as well as cloud-and precipitation-related sinks of aerosol or CDNC, can promote a more rapid SCT than fixed CDNCs in LES, as the former gives rise to more drizzle and thus an accelerated reduction in Sc cloud cover. ...
Stratocumulus (Sc) clouds and stratocumulus‐to‐cumulus transitions (SCTs) are challenging to represent in global models and they contribute to a large spread in modeled subtropical cloud feedbacks. We evaluate the impact of increasing the horizontal model resolution (∼135, 60 and 25 km, respectively) and increasing the complexity of the aerosol–cloud interaction parameterization (interactive versus non‐interactive at medium resolution) on springtime subtropical marine Sc properties and SCTs in the atmosphere‐only version of HadGEM3‐GC3.1. No significant impact on the spatial location of the SCT could be found between the different model versions. Increasing horizontal resolution led to small but significant increases in liquid water content and a stronger (more negative) shortwave (SW) cloud radiative effect (CRE), in particular over the southern‐hemisphere Sc regions. However, for two out of the four studied regions, the stronger SW CRE also brought the model outside the range of satellite‐derived values of the SW CRE. Applying non‐interactive aerosols instead of interactive aerosols also led to significantly higher liquid water content and a stronger SW CRE over the southern‐hemisphere Sc regions, while over the northern‐hemisphere Sc regions, a competition between a substantial increase in the cloud droplet number concentration and small changes in the liquid water content resulted in a weaker SW CRE or non‐significant changes. In general, using interactive instead of non‐interactive aerosol–cloud interactions brought the model closer to satellite‐retrieved mean values of the SW CRE. Our results suggest that increasing the horizontal resolution or the complexity of the aerosol–cloud parameterization has a small but statistically significant effect on the SW CRE of marine Sc, in particular over regions with high liquid water content. For these regions, the effect of introducing non‐interactive versus interactive aerosol–cloud interactions is about as large as increasing the horizontal resolution from medium to high.
... The sum of the physical tendencies in this direct approach corresponds to the "observed" apparent drying (Q 2 ; Yanai et al., 1973) for estimating the bulk effect of diabatic processes. Following Zhang et al. (2013), the water vapor budget can be written as ...
... CGILS is a long-term integration experiment to investigate the statistics for cloud fields. It simulates the cloud transition from coastal stratus to shallow cumulus offshore along the Pacific Cross-Section Intercomparison region in the north tropical to subtropical Pacific (see Fig. 4 in Zhang et al., 2013). Three locations are selected to model differ-ent regimes of clouds, i.e., shallow cumulus at CGILS-S6, stratocumulus at CGILS-S11, and well-mixed stratocumulus or coastal stratus CGILS-S12 (Table 1). ...
As a unified weather-forecast–climate model system, Global-to-Regional Integrated forecast SysTem (GRIST-A22.7.28) currently employs two separate physics suites for weather forecast and typical long-term climate simulation, respectively. Previous AMIP-style experiments have suggested that the weather (PhysW) and climate (PhysC) physics suites, when coupled to a common dynamical core, lead to different behaviors in terms of modeling clouds and precipitation. To explore the source of their discrepancies, this study compares the two suites using a single-column model (SCM). The SCM simulations demonstrate significant differences in the simulated precipitation and low clouds. Convective parameterization is found to be a key factor responsible for these differences. Compared with PhysC, parameterized convection of PhysW plays a more important role in moisture transport and rainfall formation. The convective parameterization of PhysW also better captures the onset and retreat of rainfall events, but stronger upward moisture transport largely decreases the tropical low clouds in PhysW. These features are in tune with the previous 3D AMIP simulations. Over the typical stratus-to-stratocumulus transition regime such as the Californian coast, turbulence in PhysW is weaker than that in PhysC, and shallow convection is more prone to be triggered and leads to larger ventilation above the cloud layer, reducing stratocumulus clouds there. These two suites also have intrinsic differences in the interaction between cloud microphysics and other processes, resulting in different time step sensitivities. PhysC tends to generate more stratiform clouds with decreasing time step. This is caused by separate treatment of stratiform cloud condensation and other microphysical processes, leading to a tight interaction between macrophysics and boundary layer turbulence. In PhysW, all the microphysical processes are executed at the same temporal scale, and thus no such time step sensitivity was found.
... The intercomparison study of CGILS showed that SCMs have large uncertainties in simulating stratocumulus topped boundary layer (M. Zhang et al., 2013). One possible explanation is that UNICON and stochastic UNICON do not consider downdrafts originated from the inversion. ...
We extend the previously developed stochastic unified convection scheme (UNICON) for shallow convection to deep convection by parameterizing the impact of mesoscale organized flow on updraft properties. The extended stochastic UNICON parameterizes thermodynamic properties of updrafts at the near‐surface as a multivariate Gaussian distribution, where the variances of the distribution are the summation of variances from non‐organized turbulence and mesoscale organized flow. The distribution of updraft radius is parameterized as a power‐law distribution with a scale break which is parameterized as a linear function of the strength of mesoscale organized flow. The proposed parameterization is validated using a series of large‐eddy simulations of deep convection. The free parameters introduced in the formulation of stochastic UNICON are optimized using 10 cases of single‐column model simulations over the ocean. Stochastic UNICON with the optimized parameters significantly reduces the biases of thermodynamic profiles and surface precipitation rates simulated in the original UNICON for tropical convection cases. The simulation of the variation in anomalies of temperature and moisture associated with the Madden‐Julian oscillation is also improved. The overall improvements in simulated thermodynamic profiles are found to be due to the increased heating and drying tendencies by convective processes in stochastic UNICON. An additional simulation of an idealized deep convection case shows that stochastic UNICON produces enhanced cloud variabilities with dependency on updraft radius, indicating its ability to represent the coexistence of shallow and deep convection.
... We do not have quantitative theories of their response to climate change (Bretherton, 2015), and shortcomings in their representation in climate models have long dominated uncertainties in climate projections (Cess et al., 1990(Cess et al., , 1996Bony & Dufresne, 2005;Dufresne & Bony, 2008;Vial et al., 2013;Webb et al., 2006Webb et al., , 2013Zelinka et al., 2017). Numerical experiments have been limited to studies that have explored a few dozen canonical situations, mostly in the tropics (Siebesma et al., 2003;Stevens et al., 2005;Rauber et al., 2007;Caldwell & Bretherton, 2009;Sandu & Stevens, 2011;Zhang et al., 2012Zhang et al., , 2013Blossey et al., 2013Blossey et al., , 2016Schalkwijk et al., 2015;Tan et al., 2016Tan et al., , 2017. Broader exploration has been limited by the computational expense necessary to resolve the meter-scale dynamics of low clouds in large-eddy simulations (LES). ...
Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despite their importance for climate, they remain relatively poorly understood and are inaccurately represented in climate models. A principal reason is that the high computational expense of simulating them with large-eddy simulations (LES) has inhibited broad and systematic numerical experimentation and the generation of large datasets for training parametrization schemes for climate models. Here we demonstrate LES of low clouds on Tensor Processing Units (TPUs), application-specific integrated circuits that were originally developed for machine learning applications. We show that TPUs in conjunction with tailored software implementations can be used to simulate computationally challenging stratocumulus clouds in conditions observed during the Dynamics and Chemistry of Marine Stratocumulus (DYCOMS) field study. The TPU-based LES code successfully reproduces clouds during DYCOMS and opens up the large computational resources available on TPUs to cloud simulations. The code enables unprecedented weak and strong scaling of LES, making it possible, for example, to simulate stratocumulus with $10\times$ speedup over real-time evolution in domains with a $34.7~\mathrm{km} \times 53.8~\mathrm{km}$ horizontal cross section. The results open up new avenues for computational experiments and for substantially enlarging the sample of LES available to train parameterizations of low clouds.
... In the experiment "WBF01", we revert the parameter (micro_mg_berg_eff_factor) back to 0.1 to examine its impact on the simulation of cloud phase. Second, the detrained cloud water from deep convection can substantially influence stratiform cloud microphysics and modulate the subsequent cloud microphysical processes in stratiform clouds, such as ice particle growth and precipitation formation, through its role as one of the major water sources to stratiform clouds (Zhang & Bretherton, 2008;Zhang et al., 2013). Using the new dCAPE_ULL convective trigger in EAMv2 can thus impact model convective activities and then stratiform cloud microphysical processes through detained cloud water from deep convection over the polar regions (Zhang et al., 2005). ...
This study performs a comprehensive evaluation of the simulated cloud phase in the U.S. Department of Energy, Energy Exascale Earth System Model, atmosphere model version 2 (EAMv2), and version 1 (EAMv1). Enabled by the CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation) simulator, EAMv2 and EAMv1 predicted cloud phase is compared against the GCM‐Oriented CALIPSO Cloud Product (CALIPSO‐GOCCP) at high latitudes where mixed‐phase clouds are prevalent. Our results indicate that the underestimation of cloud ice in simulated high‐latitude mixed‐phase clouds in EAMv1 has been significantly reduced in EAMv2. The increased ice clouds in the Arctic mainly result from the modification on the WBF (Wegner‐Bergeron‐Findeisen) process in EAMv2. The impact of the modified WBF process is moderately compensated by the low limit of cloud droplet number concentration in cloud microphysics and the new dCAPE_ULL trigger used in deep convection in EAMv2. Moreover, it is found that the new trigger largely contributes to the better cloud phase simulation over the Norwegian Sea and Barents Sea in the Arctic and the Southern Ocean where large errors are found in EAMv1. However, errors in simulated cloud phase in EAMv1, such as the overestimation of supercooled liquid clouds near the surface in both hemispheres and the underestimation of ice clouds over Antarctica, persist in EAMv2. This study highlights the impact of deep convection parameterizations, which has received little attention, on high‐latitude mixed‐phase clouds, and the importance of continuous improvement of cloud microphysics in climate models for accurately representing mixed‐phase clouds.
