Climate Impacts of Convective Cloud Microphysics in NCAR CAM5

Lin Lin aDepartment of Atmospheric Sciences, Texas A&M University, College Station, Texas

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Xiaohong Liu aDepartment of Atmospheric Sciences, Texas A&M University, College Station, Texas

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Qiang Fu bDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington

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Yunpeng Shan cAtmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

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Abstract

We improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. We evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSatCALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaohong Liu, xiaohong.liu@tamu.edu

Abstract

We improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. We evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSatCALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaohong Liu, xiaohong.liu@tamu.edu

1. Introduction

Deep convection is crucial in Earth’s climate. Convection and convective clouds are closely linked to global hydrological cycle. Convection accounts for more than ∼50% of the tropical total rain amount (Schumacher and Houze 2003). Most of the severe precipitation events where graupel and hail form are largely attributed to vigorous convection and are of great societal impact. In addition to their important role in the water cycle, convection and associated anvil clouds modulate Earth’s radiative energy budget through effects on incoming shortwave (SW) and outgoing longwave (LW) radiation (Chen et al. 2000). It is shown that nearly 44% of tropical cirrus clouds is generated from convective detrainment (Luo and Rossow 2004). The response and feedback of convection and the associated anvil cirrus to global warming remains an open question.

Convective parameterizations to adequately represent convective processes remain at the core of large-scale atmospheric modeling (Rio et al. 2019; Zhang and Song 2016). Conventionally, convective parameterizations used in most of the current weather and climate models (e.g., Arakawa and Schubert 1974; Gregory and Rowntree 1990; Zhang and McFarlane 1995) only provide source terms for the dynamic equations of heat, moisture and momentum altered by subgrid convection. Detailed cloud microphysics of deep convective clouds is often oversimplified or ignored. For example, in the Zhang–McFarlane deep convection scheme (Zhang and McFarlane 1995) used in the NCAR Community Atmosphere Model (CAM), rain production is empirically proportional to the vertical flux of cloud water in updrafts. This simplified rain production neglects the explicit representation of cloud droplet coalescence and fails to reflect the potential precipitation invigoration/suppression by aerosols. Additionally, number concentrations of detrained liquid droplets and ice crystals are estimated based on the assumption that detrained liquid droplets and ice crystals are monodispersed with tunable particle sizes.

Conventional global climate models (GCMs) with implicit representations of cloud microphysics in the deterministic convection schemes often show a common bias in precipitation frequency of too much light precipitation and too little heavy precipitation (Dai 2006; Sun et al. 2006). Kang et al. (2015) improved the simulations of heavy precipitation by including explicit cloud microphysics to a GCM, suggesting that an explicit representation of convective cloud microphysics is a key ingredient for simulating realistic precipitation frequency. Zhang et al. (2005) extended a microphysics scheme used for stratiform clouds to simulations of cloud microphysical processes for convective clouds in the ECHAM5 model, enabling investigations of convection–aerosol interactions. More recently, Song and Zhang (2011, hereafter SZ11) developed a cloud microphysical scheme for convective clouds in the NCAR CAM model. Storer et al. (2015) showed increases in the convective detrainment and large-scale precipitation through implementing a double-moment convective cloud microphysics scheme in NCAR CAM5. As they demonstrated the effects of convective microphysics parameterizations on the hydrological cycle, an improvement in the partitioning between convective and large-scale precipitation was also achieved.

Significant progress has often been made toward improving the treatments of large-scale (stratiform) cloud microphysics in GCMs (e.g., Gettelman and Morrison 2015; Gettelman et al. 2019; Liu et al. 2007). But improving the parameterizations of convective cloud microphysics is still one of the major tasks in GCM developments to better represent the interactions of convection with large-scale clouds and the response of convection to aerosol perturbations. One general issue of current convective cloud microphysics schemes is that representations of cloud microphysical process rates are mostly adopted from stratiform cloud microphysics schemes. Consequently, some of the microphysical parameterizations can hardly reflect the fast microphysical processes in convective clouds. For example, the commonly used autoconversion parameterization developed by Khairoutdinov and Kogan (2000, hereafter KK) is for stratocumulus clouds, and this parameterization likely underestimates the rain production in convective clouds.

In addition to warm clouds, several aspects of mixed-phase and ice-phase clouds in deep convection also need additional attention, including the treatments of ice hydrometeor sedimentation and detrainment, and the representation of precipitating hydrometeors. Ice sedimentation appears to be a leading factor known to have a large impact on climate simulations (Hourdin et al. 2017; Mitchell et al. 2008). Sanderson et al. (2008) evaluated the relative impacts of different cloud parameters on climate sensitivity in a GCM using an ensemble of perturbed climate simulations and identified ice particle fall speed as one of the two most influential factors. Therefore, it is necessary to carefully examine the treatments of terminal velocity of ice hydrometeor in convective microphysics schemes.

Rimed ice particles (i.e., graupel and hail) grow primarily through riming by which ice particles collect supercooled cloud droplets. One of the largest differences between deep convective and stratiform clouds is that riming processes in deep convection are much stronger. Deep convection featured with strong updrafts provides a favorable environment for producing rimed ice hydrometeors which are characterized by large fall speeds. It might be legitimate to neglect rimed ice in stratiform microphysics schemes, whereas rimed ice needs to be considered in deep convective cloud microphysics schemes. However, the rimed ice hydrometeors are often neglected in current convective microphysics schemes (e.g., Zhang et al. 2005; Song and Zhang 2011). By noting the importance of incorporating rimed ice hydrometeors for properly simulating convective cloud properties such as the vertical distribution of cloud water (Lin et al. 2011; Wu et al. 2013), Lin et al. (2021, hereafter L21) recently implemented the graupel-related microphysics into a convective cloud microphysics scheme in NCAR CAM5.3 and alleviated the overestimation of ice water content in the upper troposphere.

L21 modified and improved the SZ11 convective microphysics scheme in the following aspects: 1) cloud ice was allowed to sediment with the cloud ice terminal velocity formulated in terms of Davis (X) and Reynolds (Re) numbers; and the original snow terminal velocity parameterization in SZ11 was replaced with a parameterization that was developed based on in situ aircraft observations adjacent to convective cores (Elsaesser et al. 2017a, hereafter EL17); 2) graupel was added as a new prognostic hydrometer to the existing four-class (cloud liquid, cloud ice, rain, and snow) convective microphysics scheme; and 3) convective snow was allowed to detrain into the stratiform clouds. In this study, rain initiation and generation rates in warm clouds are also modified, guided by satellite estimates of cloud liquid water path. Note that L21 developed the scheme using a single-column model focusing on convective clouds detected during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) campaign. As a follow up of L21, this study evaluates the global impacts of the above modifications and improvements on simulated clouds, precipitation, and climate, and compares the global model simulations against observations.

The rest of the manuscript is organized as follows. Section 2 describes the GCM and convective microphysics parameterizations employed. Section 3 presents the observational datasets used for model evaluation. Section 4 shows and discusses the impact of new convective microphysics scheme on climate simulations focusing on graupel microphysics, vertical distribution of convective frozen water mass, and precipitation statistics. A summary and conclusions are given in section 5.

2. Model configuration and scheme description

a. The CAM5.3 with default convective microphysics scheme

CAM5.3 is the atmosphere component of the National Center for Atmospheric Research Community Earth System Model, version 1.2 (CESM1.2) (Hurrell et al. 2013; Neale et al. 2012). In the standard CAM5.3, clouds are divided into three categories (i.e., large-scale stratiform clouds, shallow convective clouds, and deep convective clouds) and treated by separate but collaborative cloud parameterizations. Representation of stratiform clouds includes an explicit double-moment cloud microphysics scheme (Morrison and Gettelman 2008, hereafter MG08) that predicts mass mixing ratios and number concentrations of cloud hydrometeors. Shallow convection is parameterized using a mass-flux scheme combined with a buoyancy sorting algorithm (Park and Bretherton 2009). Deep convective clouds are parameterized by a bulk mass-flux scheme originally developed by Zhang and McFarlane (1995, hereafter ZM95) with further modifications on the entrainment-dilute calculation of the convective available potential energy (CAPE) (Neale et al. 2008). ZM95 lacks explicit representations of cloud microphysical processes in deep convective clouds. The total condensate detrained from convective cores to stratiform clouds is partitioned into liquid and ice over a tunable temperature range of −5°C < T < −35°C. ZM95 detrains cloud liquid and cloud ice but not snow. Our model experiment using the default CAM5.3 is referred to as the “ZM” for the remainder of this paper as it highlights the ZM95 deep convection scheme is used. A summary of model experiments is given in Table 1.

Table 1

Summary of model simulations.

Table 1

Song and Zhang (2011, hereafter SZ11) introduced a double-moment four-class (cloud liquid, cloud ice, rain, and snow) convective microphysics scheme and coupled it with ZM95. SZ11 includes explicit microphysical processes such as autoconversion, accretion, homogeneous and heterogeneous freezing, rain and snow sedimentation, ice nucleation, and droplet activation. Convective updraft velocity is parameterized based on the updraft kinetic energy budget equation (see SZ11 for more details) and used to estimate activated cloud condensation nuclei and ice nuclei. Moreover, cloud liquid and cloud ice are assumed to be suspended, and precipitating particles (rain and snow) sediment to the ground once they form. Convective cloud liquid droplets and cloud ice crystals are radiatively active. The default CAM5.3 physics, with the SZ11 convective microphysics scheme turned on, is used for a model experiment referred to as “SZ” (Table 1).

b. The improved convective microphysics scheme

Herein we briefly describe the modifications made to the SZ11 scheme. More details can be found in L21.

1) Parameterizations of convective ice and snow terminal velocities

Convective cloud ice is allowed to sediment and detrain to the stratiform cloud area in L21. The parameterization of convective cloud ice terminal velocity follows the X-Re terminal velocity formulation based on the small ice crystal assumption (L21). Hereafter, we refer to this X-Re scheme as the “XReICE” for simplicity. The factors α and β in the X-Re scheme for the terminal velocity–diameter power-law relationship are dependent on both ice particle (e.g., mass, size) and airflow properties (e.g., viscosity). Meanwhile, the original convective snow terminal velocity parameterization in SZ11 is replaced by the EL17 scheme, in which the snowfall speed is parameterized as a joint function of temperature, pressure, and snow water content. We refer to the model experiment using the XReICE as well as the EL17 schemes as “XReICE_EL17” (Table 1).

2) Graupel microphysics

Moreover, microphysical processes associated with graupel formation are added into the SZ11. The production of graupel is from the accretion of cloud droplets by snow, collection of rain by snow, collection of snow by rain, freezing of rainwater, and accretions of cloud liquid/rainwater by graupel. The loss of graupel is through its sedimentation. Unlike most previous regional and global modeling studies (e.g., Gettelman et al. 2019; Lin et al. 1983; Reisner et al. 1998) that treat rimed ice (graupel and hail) spectrum with the inverse-exponential distribution (i.e., shape parameter μ = 0), we prescribe μ to be 3 in a gamma distribution function to represent graupel size distribution. This is because a gamma function with a nonzero μ outperforms the inverse-exponential distribution function in fitting the precipitating particle spectra (Shan et al. 2020). We term the model simulation with graupel microphysics on top of “XReICE_EL17” as “XReICE_EL17_rime” (Table 1).

3) Convective snow detrainment

Furthermore, snow in convective clouds is allowed to detrain to feed stratiform clouds. We term this model simulation on top of “XReICE_EL17_rime” as the “Conv_snow_detr” (Table 1).

4) Warm rain formation in convective clouds

Apart from the aforementioned modifications for convective ice microphysics, we have also modified the warm-rain initiation and generation processes (i.e., autoconversion and accretion) guided by satellite estimates of cloud liquid water path (LWP) climatology (Elsaesser et al. 2017b) and CERES-EBAF TOA radiation budget (Loeb et al. 2018) (see method and Figs. S1 and S3 in the online supplemental material). The KK parameterizations of autoconversion and accretion were developed for marine stratocumulus clouds and failed to reflect the fast autoconversion and accretion rates in deep convective clouds with strong updrafts and large liquid condensate mass (Khvorostyanov and Curry 2014). Meanwhile, the SZ experiment shows unrealistically high LWP at mid and low latitude regions (see Figs. S1 and S2), which supports the hypothesis that liquid water in convective clouds is not efficiently converted into precipitation. The high LWP bias in the SZ experiment results in a low bias of shortwave (SW) radiation flux at top of the atmosphere (TOA) by ∼ −12 W m−2, which is consistent with the TOA SW bias of ∼ −17 W m−2 as reported by Song et al. (2012, see their Fig. 15). We modify the parameters in autoconversion and accretion parameterizations (i.e., αauto and αacc and see method in supplemental material), which efficiently decrease liquid condensate mass in deep convective clouds and also restore the TOA radiative energy balance, in line with the estimates of LWP climatology and CERES-EBAF TOA radiation budget (see Fig. S3). The model experiment in which SZ11 warm rain processes are adjusted is referred to as “SZaftu.” Note that except for the ZM and SZ experiments, all the other experiments possess this tuning trace (Table 1).

Information for all the six experiments is summarized in Table 1. CAM5.3 has 30 vertical layers, and a horizontal resolution of 1.9° × 2.5°. The model is run with the finite-volume (FV) dynamical core using a dynamical time step of 30 min. All 6-yr simulations are forced by prescribed, seasonally varying present-day climatological sea surface temperatures and sea ice extent, recycled yearly. The first year in all the simulations is used as spinup.

3. Observational datasets and model–observation comparison

For model evaluation, the following datasets are used. Clouds and the Earth’s Radiant Energy System (CERES)–Energy Balanced and Filled (EBAF) L3b product (Loeb et al. 2018) is used for constraining cloud radiative forcing and TOA radiative energy balance. The Tropical Rainfall Measuring Mission (TRMM) 3B42 (version 7) L3 3-hourly observations at a resolution of 0.25° × 0.25° over 50°S–50°N (Huffman et al. 2007) are used for evaluating the precipitation intensity frequency. The Global Precipitation Climatology Project (GPCP) monthly precipitation product version 2.3 at a resolution of 2.5° × 2.5° (Adler et al. 2018) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) monthly precipitation product (enhanced version) at a resolution of 2.5° × 2.5° (Xie and Arkin 1997) are used for evaluating the precipitation mean state. To make the comparison consistent between observations and model simulations, the model data are output with the same frequencies as those in the corresponding observations. TRMM observations are regridded to the same 1.9° × 2.5° grids as CAM5.3; CAM5.3 simulations are regridded to the 2.5° × 2.5° grids as GPCP and CMAP observations. Large-scale forcing data during the intensive operational periods (IOPs) of TWP-ICE (conducted near Darwin, Northern Australia, in early 2006) and the Green Ocean Amazon (GOAmazon2014/5, conducted from 15 February to 26 March 2014 for the wet season and from 1 September to 10 October 2014 for the dry season near Manaus, Brazil) campaigns are used for studying the relationship between convection available potential energy (CAPE) and precipitation.

The CloudSat–CALIPSO-derived profiles of cloud type classification from the 2B-CLDCLASS-lidar product version R05 (Sassen et al. 2008; Sassen and Wang 2008; Wang 2019) and ice water content (IWC) from the CloudSat–CALIPSO combined level-2C Ice Cloud Property Product (2C-ICE) version R05 (Deng et al. 2010, 2013, 2015; Mace and Deng 2019) from 2007 to 2009 are used for evaluation of convective ice cloud condensate. Collocation between 2B-CLDCLASS-lidar and 2C-ICE products to obtain a subset of IWC estimates specifically for deep convective clouds is described here. To identify deep convective clouds in observations and model simulations, the following conditions are required: 1) cloud-top height ≥ 6 km; 2) cloud-base height ≤ 2 km (Takahashi and Luo 2014; Wang and Zhang 2018). Vertical profiles of cloud type classification from 2B-CLDCLASS-lidar product and the associated IWC from 2C-ICE product from a granule on day 153 of year 2010 are shown in Fig. 1. A deep convective system stretching from near the surface to 16 km is detected at ∼3100 s (Fig. 1, left); based on the cloud type classification information, the columns of IWC belonging to the deep convective cloud collocated at ∼3100 s (Fig. 1, right) are collected. Cloud type classification information is used only when the cloud type quality indicator is greater than 0.6 for high confidence level retrievals. A data quality flag is also used to indicate the data that are of good quality for use. There are a total of ∼3 081 974 deep convective scenes identified by 2B-CLDCLASS-lidar product during 2007–09, ∼65% of which are located over the tropics (20°S–20°N). Deep convective IWC collected along swaths are regridded into the 1.9° × 2.5° grids and binned into monthly data. Then the 1.9° × 2.5° grids are average estimates of all cloudy scenes. To compare with CloudSat–CALIPSO observations in a more consistent manner, we apply a subsampling technique for processing the in-cloud IWC from the coarse resolution model to approximate the subgrid-scale statistics. We divide CAM5 grid boxes into 100 subcolumns based on the model cloud fraction profile assuming the maximum-random overlap (Jakob and Klein 1999; Collins 2001). The cloud properties remain homogeneous across the cloudy subcolumns for each given vertical level, which leads to 100 subcolumns with varying cloud profiles (Fig. S6). Model IWCs are then averaged across the cloudy subcolumns and compared to CloudSat–CALIPSO observations. This subsampling method was used in previous GCM-satellite comparison studies (e.g., Berry et al. 2019, 2020).

Fig. 1.
Fig. 1.

(left) Time–height cross section of cloud type classification from the 2B-CLDCLASS-lidar product and (right) the associated cloud ice water content (g m−3) from the 2C-ICE product. In the left panel, white, purple, red, yellow, green, blue, cyan, brown, and black represent clear sky, cirrus, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convective clouds, respectively. Two red dashed curves in the right panel indicate the subfreezing and −37°C isotherms. Granule data from 2 Jun 2010 (i.e., day 153 of year 2010) are taken as an example for demonstration purpose.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

The deep convection associated with heavy precipitation presents a challenge for remote sensing retrievals. Caution should be exercised when using 2C-ICE IWC retrievals at the lower part of convective clouds because of the issue of radar signal attenuation. Therefore, we focus our comparisons of the simulated ice mass with 2C-ICE retrievals in the middle and upper altitudes of deep convective clouds for which the retrieved microphysics are more certain (Deng et al. 2013).

4. Results

a. Warm rain processes and cloud radiative forcing

Figure 2 shows zonally averaged shortwave cloud forcing (SWCF) and longwave cloud forcing (LWCF) from CERES-EBAF estimates and model simulations. It is found that the SZ experiment produces too strong (too negative) SWCF over the tropics and midlatitudes (Fig. 2). The SZ experiment produces the strongest SWCF over the tropics, in contrast to CERES-EBAF, which suggests Southern Ocean clouds scatter shortwave radiation the most. LWCF in the SZ experiment agrees with CERES-EBAF estimates. One possible reason for the strong SWCF is due to excessive tropical convective cloud liquid, as discussed in section 2b(4) and in supplemental material. We enhance the rain initiation and production rates in deep convective clouds and to a large extent reduce SWCF biases as shown in the SZaftu experiment (Fig. 2). While we call for a better constraint on warm rain processes in deep convective clouds for future studies, this study clearly shows an improved performance of simulated TOA radiation budget (Fig. S3), SWCF (Fig. 2), LWP (Fig. S1), and tropical precipitation (as discussed in section 4c), when KK autoconversion and accretion parameterizations are modified for convective clouds.

Fig. 2.
Fig. 2.

Zonal distribution of shortwave cloud forcing (SWCF) and longwave cloud forcing (LWCF) from CERES-EBAF observational estimates (black dashed curve) as well as model simulations. Experiment acronyms are found in Table 1. Green shading corresponds to an observation uncertainty of ±4 W m−2 (Hourdin et al. 2017).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

b. Simulations of convective ice microphysics

1) Graupel occurrence

Figure 3 illustrates the zonal mean latitude–height distribution of occurrence frequency of graupel (FREQG) for June–August (JJA; Fig. 3a) and December–February (DJF; Fig. 3b). Also shown are the vertical profiles of graupel water content over the midlatitudes in both hemispheres from the XReICE_EL17_rime experiment. FREQG is calculated as the temporal fractional occurrence of graupel across model time steps. The graupel water content is averaged conditionally on the presence of graupel in convective clouds.

Fig. 3.
Fig. 3.

Zonal mean (a),(b) latitude–height distributions of FREQG and (c)–(f) vertical profiles of simulated graupel water content (g m−3) from the XReICE_EL17_rime simulation in June–July–August (JJA) and December–January–February (DJF). FREQG = occurrence frequency of graupel. Vertical profiles of graupel water content (g m−3) in convective clouds at 60°–30°S are shown during JJA in (c) and DJF in (e). Vertical profiles of graupel water content at 30°–60°N are shown during JJA in (d) and DJF in (f).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

One of the FREQG peaks occurs at ∼8 km in the summer hemisphere tropics and subtropics, with peak values of just over 10% in JJA and 5%–10% in DJF (Figs. 3a,b). Other FREQG peak occurrences are found in the midlatitude at lower altitudes with peak values of about 15%–30% (Figs. 3a,b). Seasonally, high FREQG in midlatitudes is usually found in the winter hemisphere (i.e., southern midlatitude in JJA and northern midlatitude in DJF).

The peak values of graupel water content over midlatitude in both summer and winter hemispheres (Figs. 3c–f) are comparable to that over the tropics (Fig. 5c) but are located at a lower altitude. Despite the high FREQG below 2.5 km, the corresponding graupel mass is negligible. The spatial pattern of FREQG and graupel water content exhibit interhemispheric asymmetry. For the graupel water content in summer versus winter in the Northern Hemisphere (NH) (Fig. 3d versus Fig. 3f), convection generating graupel can penetrate higher altitudes till about 17 km in summer versus about 12.5 km in winter. Moreover, comparing the NH versus the SH during the respective summer (Fig. 3d versus Fig. 3e) and winter (Fig. 3c versus Fig. 3f), NH convection usually reaches higher in altitude than SH, especially in the summer, due to land and ocean contrast.

Figure 4 shows the geographic distributions of FREQG at 8 km and near the surface in two seasons from model simulations. In the tropics, graupel is found in regions with deep convection, mainly over the central Africa, Indian Ocean, Maritime Continent, tropical western Pacific, South Pacific convergence zone (SPCZ), the intertropical convergence zone (ITCZ), and Amazon basin (Figs. 4a,b). In the subtropics, high graupel occurrence at 8 km is associated with monsoon activities as well as complex terrains, with active regions including East Asia, the Tibetan Plateau, and Rocky Mountains regions in the NH summer (Fig. 4a) and the southern Africa and Andes Mountains regions in the SH summer (Fig. 4b). The XReICE_EL17_rime successfully reproduces graupel occurrence in the regions favorable for severe thunderstorm and hail (Brooks et al. 2003; Zipser et al. 2006; Zhang et al. 2008).

Fig. 4.
Fig. 4.

Geographic distribution of FREQG from the XReICE_EL17_rime simulation at (a),(b) 8 and (c),(d) 1.5 km in (left) JJA and (right) DJF.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

We collect regional and global reports of graupel/hail occurrence in previous studies for a qualitative evaluation (Table 2). The XReICE_EL17_rime experiment simulates high FREQG over continents, such as over the Tibetan Plateau, central and Southeast United States, Argentina, tropical Africa, and the NH high latitudes, in agreement with previous studies (Cintineo et al. 2012; Mezher et al. 2012; Murillo et al. 2021; Zhang et al. 2008; Bang and Cecil 2019; Cecil and Blankenship 2012). Hail was reported over most of the Australian continent, with a peak in the northwest, which is also captured by our simulation (Fig. 4b). However, there are some notable differences in graupel occurrence over tropical and midlatitude open oceans between the XReICE_EL17_rime experiment and satellite retrievals (e.g., a global climatology of severe hailstorm survey, Cecil and Blankenship 2012). Occasional hailstorms are spread across tropical oceans while none are retrieved poleward of ∼40°S by passive microwave imagers, in large contrast to high FREQG values in Figs. 4c and 4d.

Table 2

Summary of graupel occurrence reported from literature, to which our model simulations are compared qualitatively. Y: Supported by a global climatology of severe hailstorm survey (Cecil and Blankenship 2012). X: Not reported by a global climatology of severe hailstorm survey (Cecil and Blankenship 2012).

Table 2

However, we cannot simply regard the simulated high FREQG values over ∼40°S as an overestimation. First, Cecil and Blankenship (2012)’s hail climatology is limited to large hail sizes, while graupel and small hail which are not included in their retrievals might occur over tropical and midlatitude oceans. Second, Cecil and Blankenship suspected that their estimates for the tropics and open oceans are too conservative, making it possible for some of the tropical and oceanic storms not to be included. Note a very small graupel water content despite a high occurrence frequency at 1.5 km near 50°S (Fig. 3). We also note that the spatial distribution of FREQG by the XReICE_EL17_rime experiment is similar to that reported by Gettelman et al. (2019), who implemented rimed ice (graupel and hail) microphysics in the stratiform cloud microphysics scheme in CAM6.

On the other hand, higher graupel occurrence over oceanic storm tracks found in this study is probably due to the frequent occurrence of mixed-phase clouds. Graupel production is parameterized to depend on the presence of mixed-phase clouds and the difference in collector and collected hydrometeor terminal velocities, similar to Gettelman et al. (2019) and many other schemes such as Lin et al. (1983) and Reisner et al. (1998). From the perspective of model parameterizations, the way graupel is parameterized together with the evidence of high occurrence of mixed-phase clouds (see Fig. S7) likely explain the simulated high occurrence of graupel over oceanic storm tracks in both hemispheres. Mixed-phase clouds are ubiquitous in the extratropics and poles revealed by observations (Cesana et al. 2012). Recent measurements also reported frequent riming events in the high-latitude mixed-phase clouds (Fitch and Garrett 2022a,b; Oue et al. 2015).

Lightning climatology can partly complement the hail climatology studies to indicate the presence of graupel (riming processes). Lightning climatology (https://ghrc.nsstc.nasa.gov/lightning/data/data_lis_otd-climatology.html, also see Fig. S8) generally shows more activity in the tropics and open oceans compared to hail climatology (D. Cecil 2021, personal communication), suggesting the presence of graupel over tropical and midlatitude oceans. We also provide an annual mean FREQG at 8 km from the XReICE_EL17_rime experiment (Fig. S9), which shows a direct and good comparison with the lightning climatology (Fig. S8) over the tropical oceans. Note that not all riming processes would be associated with lightning. Graupel can be produced with relatively weak updrafts that would not generate electric charge at a fast enough rate to support electric field breakdown (lightning). And many oceanic convective clouds are weakly electrified, with graupel produced but no lightning. There are scarce reports of hail occurrence over open oceans, which leaves the high FREQG over open oceans loosely constrained.

2) Convective ice mass

Figure 5 shows the cloud ice, snow and graupel water content profiles in convective clouds averaged over the tropics (20°S–20°N) from model simulations. The peak of cloud ice water content of ∼0.13 g m−3 located at about 9 km is largely consistent with the corresponding peak value reported by Song et al. (2012) (see their Fig. 2). Enhancement in warm rain imposes little impact on the vertical profiles of convective cloud ice content (comparing the SZaftu and SZ experiments). Cloud ice water content increases above ∼12 km when graupel microphysics is included in the XReICE_EL17_rime and Conv_snow_detr experiments as compared to the other experiments without graupel microphysics, consistent with Gettelman et al. (2019).

Fig. 5.
Fig. 5.

Vertical profiles of simulated cloud ice, snow, and graupel water content (g m−3) in convective clouds averaged over the tropics (20°S–20°N).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

Note that the modifications of ice microphysics [sections 2b(1)–(3)] have a small impact on the radiation budget (Fig. 2) despite large changes in snow and graupel mixing ratios (Fig. 5). This result can be explained as follows. First, in current CAM, only convective cloud ice is radiatively active while the precipitating particles including snow and graupels are not coupled to the radiation scheme. There is little change in convective cloud ice mixing ratios in various experiments (Fig. 5a). Second, despite the large convective snow mixing ratios in SZ, SZaftu and XReICE_EL17 experiments (Fig. 5b), they are not only radiatively inactive but also do not detrain to contribute to the stratiform clouds. On the other hand, in the Conv_snow_detr experiment, although the detrained part of convective snow becomes radiatively active, its impact on radiation is expected to be small because of a very small convective snow mixing ratios in this experiment (Fig. 5b).

Vertical profiles of convective snow water content show peak values of 1.8 and 1.6 g m−3, respectively, at about 9 km in the SZ and SZaftu experiments. Consistent with the single column model (SCM) testing in Lin et al. (2021), replacing the default convective snow terminal velocity parameterization with the EL17 parameterization (XReICE_EL17 experiment) decreases snow water content in the deep convective cloud layers, particularly in the mid and upper portions. The main difference between simulations without graupel versus simulations with graupel is a large reduction in snow mass above the melting level at ∼5 km (Fig. 5b), which is associated with the production and growth of graupel (Fig. 5c). Allowing convective snow to detrain from the deep convective clouds to the large-scale clouds (Conv_snow_detr experiment) further decreases convective snow water content (Fig. 5b).

The peak of graupel mass over tropics (Fig. 5c) coincides with the tropical FREQG peak (Fig. 3). The graupel mass peaks at the same altitude as cloud ice and snow mass. The inclusion of graupel induces some nonlinear changes in the partitioning between cloud ice, snow and graupel that might affect water budget in the upper troposphere. Note that the gain of graupel mass is much less than the loss of snow mass above 9 km when graupel microphysics is included (XReICE_EL17 versus XReICE_EL17_rime experiments); in other words, the total frozen water content (sum of cloud ice, snow and graupel mass) is significantly reduced in the mid- and upper troposphere when graupel microphysics is included, which will be further discussed below.

Next, we evaluate the simulated vertical distributions of frozen water content (FWC, the sum of suspending and precipitating ice) in convective clouds against CloudSat–CALIPSO retrievals. We combine the simulated suspending cloud ice and precipitating ice (i.e., snow and graupel) because observations cannot robustly distinguish between them. Figure 6 shows the latitude–height distributions of the annual zonally averaged convective FWC from the simulations and the CloudSat–CALIPSO deep convective estimate (labeled as “DP 2C-ICE”). Figure 7 shows the vertical profiles of convective FWC from model simulations and CloudSat–CALIPSO retrievals over selected regions during their convectively active seasons. The profiles are averaged in six major tropical and midlatitude convective regions: the tropical region for all year, Amazon, the tropical western Pacific (TWP), and India for June–September, the Maritime Continent (MC) for May–August, and the Southern Great Plains (SGP) for May–August.

Fig. 6.
Fig. 6.

Zonal annual mean latitude–height distributions of convective frozen water content (FWC) from CloudSat–CALIPSO-retrieved deep convective FWC and model simulations. Isotherms of −37°C (upper black dashed curve) and 0°C (lower black dashed curve) are also plotted.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

Fig. 7.
Fig. 7.

Vertical profiles of convective frozen water content (FWC) from CloudSat–CALIPSO-retrieved deep convective FWC and model simulations over the tropics (20°S–20°N; annual mean), the Amazon (20°S–5°N, 40°–80°W; for June–September), the tropical western Pacific (TWP; 0°–15°N, 130°–170°E; for June–September), India (14°–26.5°N, 74.5°–94°E; for June–September), the Maritime Continent (MC; 10°S–10°N, 90°–160°E; for May–August), and the Southern Great Plains (SGP; 37°–42°N, 90°–110°W; for May–August).

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

In general, the model can broadly reproduce the zonal distributions of convective FWC. However, there is an obvious discrepancy between the observational estimates and model simulations. The majority of observed deep convective FWC is confined within the mixed-phase region (between the −37° and 0°C isotherms). However, the SZ experiment simulates most of the deep convective FWC around the homogeneous freezing level. The SZ experiment overestimates convective FWC by more than a factor of 2 at ∼8 km and by more than a factor of ∼5 at about 12 km and above when compared with DP 2C-ICE retrievals (Figs. 6 and 7). The overestimation of convective FWC in the mid and upper troposphere is consistent with the SCM study (Lin et al. 2021). The enhancement in warm rain processes in the SZaftu experiment reduces the FWC peak at ∼8 km. The simulated terminal velocity of convective snow in XReICE_EL17 is faster than that in SZ at a given snow particle size (see Fig. 2b in Lin et al. 2021) due to the use of the EL17 terminal velocity scheme. The accelerated sedimentation of snow particles at a lower altitude prevents snow particles from being transported further along updrafts, resulting in a subsequent reduction of snow mass at higher altitudes. Since cloud ice mass itself accounts for a small portion of FWC (Fig. 2a) and the convective cloud ice mass-weighted terminal velocity is on the order of 0.15 m s−1 (see Fig. 3a in Lin et al. 2021), the added cloud ice sedimentation has a small effect on reducing the convective FWC overestimate.

Inclusion of graupel as a new cloud hydrometeor in the convective cloud microphysics scheme in the XReICE_EL17_rime experiment efficiently decreases FWC throughout the troposphere especially in the upper levels (Figs. 6 and 7). By including graupel in convective cloud microphysics scheme, the transformation of snow to graupel significantly impacts the vertical distributions of FWC (Figs. 57), as snow has a slower mass-weighted mean terminal velocity (1–2 m s−1) than graupel (up to ∼6 m s−1) (see Figs. 3b,c in Lin et al. 2021). The simulated vertical distributions of FWC in the XReICE_EL17_rime experiment exhibit much stronger vertical decays away from the FWC peaks toward the cloud top. The maximum of FWC is now confined within the mixed-phase region, in better agreement with DP 2C-ICE retrievals than the simulations without graupel microphysics. Convective snow detrainment to stratiform clouds is a sink term for convective snow. As a result, allowing convective snow to detrain further decreases the convective FWC especially in the upper cloud levels.

Figures 6 and 7 clearly show the improvement of FWC simulations in the middle and upper troposphere across different convective regions with the improved convective cloud microphysics scheme. It is highlighted that inclusion of graupel microphysics plays the most important role in reducing the convective FWC overestimation.

Our modified convective microphysics schemes may still overestimate FWC in the mid and upper troposphere, and the simulated FWC peaks are ∼2 km higher than what DP 2C-ICE sees. Note that CloudSat–CALIPSO’s equator crossing times are ∼0130 and 1330 local solar time, with sampling limited to late night and early afternoon. The late-night and early-afternoon sampling introduces a potential diurnal sampling bias. Over the tropics, observations of convective activities are collected a few hours earlier than the corresponding day and night peak convective activities. Consequently, the convective FWC that we analyze may tend to be somewhat smaller/weaker and locate at lower altitudes than what would have been observed a few hours later on the local day. Moreover, differences in assumptions of the microphysics between the 2C-ICE retrieval algorithm and convective ice microphysics parameterizations might contribute to the discrepancy between observations and model. The 2C-ICE retrieval algorithm uses a gamma particle size distribution (PSD) while the improved convective microphysics assumes the Marshall–Palmer PSD for cloud ice and snow particles, and a gamma PSD for graupel. Apart from PSD, Deng et al. (2013) pointed out that the uncertainty in simulated radar reflectivity due to particle habit assumptions is larger than the PSD assumptions. Convective microphysics assume spherical ice particles while 2C-ICE assumes a habit-mixture in the retrieval algorithm (an error of about 2.5 dB in Ze due to the habit assumption is assigned; Mace and Deng 2019).

This study does not apply the COSP satellite simulator for CloudSat–CALIPSO satellites (e.g., Bodas-Salcedo et al. 2011). First, to our knowledge, there are potential inconsistencies between the cloud microphysics scheme used in the host model and the cloud microphysics scheme used in the instrument simulator. For example, Riley Dellaripa et al. (2021) described modifications made to COSP and Quickbeam for consistency with CAM5 microphysics, including assumptions in hydrometeor particle size distributions, convective rain and snow effective radii and number concentrations, and subgrid sampling algorithm. Second, cloud properties from a mixture of cloud types within each grid box feed into the codes of instrument simulator. There is no separation among different cloud types. However, the objective of this study is to evaluate the impacts of modifications in the convective cloud microphysics parameterizations on the simulated deep convective clouds and climate. For both observations and model output, we target the variables solely from deep convective clouds. Based on these considerations, we do not use the COSP simulator in the current study. Instead, we employ a subcolumn approach (see section 3) that is standard in model–measurement comparison studies. Berry et al. (2019) demonstrated that the subcolumn method produces similar results to what a satellite simulator would accomplish.

Nonetheless, some useful broad tendencies can still be derived from the current comparison. The overestimation of convective FWC is also shown in a SCM study during the TWP-ICE experiment (Lin et al. 2021), which evaluated model simulations of convective FWC against geostationary satellite and ground-based radar combined retrievals (Seo and Liu 2005, 2006). Thus, we can conclude that the simulated convective FWC is overestimated by the original SZ11 convective microphysics scheme and the new schemes with the graupel microphysics substantially reduce the model FWC bias.

c. Precipitation

In this section, the probability distribution function (PDF) of precipitation intensity as well as the spatial distribution of frequency of precipitation intensity in different ranges are evaluated against the TRMM 3-hourly observations. In addition to the experiments by CAM5.3 with different modifications of the convective cloud microphysics (Table 1), this section also includes the comparison with results from the reference ZM experiment.

Figure 8 shows the PDF of the total precipitation intensity over the tropics (20°S–20°N) from the TRMM observations and model simulations. Also shown are the PDFs of large-scale stratiform and convective precipitation intensities. The PDFs are generated using an equal-interval intensity bin of 0.5 mm day−1 from 0 to 200 mm day−1. The ZM experiment underestimates the frequency of heavy precipitation and overestimates the frequency of light precipitation, which is a well-known bias of “too much light precipitation and too little heavy precipitation” when using the deterministic convection schemes in most GCMs (e.g., Dai 2006; Kang et al. 2015; Zhang and Mu 2005; Na et al. 2020). The use of the SZ11 convective microphysics scheme (the SZ experiment) mitigates this bias to some extent, leading to a decrease in the frequency of precipitation intensity less than 20 mm day−1 and an increase in the frequency of precipitation intensity larger than 20 mm day−1 compared to the ZM experiment. The breakdown of total precipitation into the convective and large-scale precipitation shown in Fig. 8b reveals that the reduction of light precipitation frequency is entirely from convective precipitation; the increase in intense precipitation frequency is largely from convective precipitation. The SZ11 convective microphysics with enhancement in warm rain processes (the SZaftu experiment) further decreases the frequency of light precipitation and increases the frequency of precipitation intensity greater than 40 mm day−1 compared to the SZ experiment. Similarly, the decrease in the frequency of light total precipitation and increase in the frequency of intense total precipitation primarily come from the changes in convective precipitation.

Fig. 8.
Fig. 8.

Frequency distributions of (a) total and (b) convective (solid line) and large-scale (dashed line) precipitation intensity over the tropics (20°S–20°N) for TRMM observations and model simulations.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

The simulated precipitation frequency is relatively insensitive to the changes in ice microphysical processes, as shown by the small differences among the simulated precipitation frequencies by SZaftu, XReICE_EL17_rime and Conv_snow_detr experiments. Rather it is quite sensitive to the precipitation generation processes of liquid water conversion to rainwater (SZ versus SZaftu experiments). This is because snow and graupel dominate the frozen water mass mixing ratio (Fig. 5). On the other hand, the convective cloud liquid and rain mass mixing ratios are comparable (not shown). With convective cloud liquid assumed to suspend in the atmosphere, changing the conversion rate of cloud liquid to rainwater can significantly impact the precipitation intensity. Comparison with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) dataset 1988–2016 (Elsaesser et al. 2017b) shows that the model holds too much suspended liquid in air in the SZ experiment (Fig. S1). Accelerating the conversion of convective cloud liquid to rainwater in the SZaftu experiment reduces model LWP bias and produces a greater amount of precipitating hydrometeor which leads to higher frequency of occurrence of intense precipitation. A lack of the intense precipitation is partially corrected by removing the overestimated suspending cloud liquid. And we show that accelerating the conversion of convective cloud liquid to rainwater in the convective microphysics scheme as we attempt in this study is in the correct direction to reproduce the intense precipitation events as seen in the TRMM observations. Success in reproducing the intense precipitation events also proves that the KK warm rain parameterizations may not be suitable for convective clouds. Unfortunately, to our knowledge, parameterizations of autoconversion and accretion suitable for deep convective clouds based on statistical analysis of large eddy simulations with size resolved cloud microphysics schemes are not available (Y. Kogan 2021, personal communication). The intense precipitation frequency in the SZaftu experiment still misses the TRMM observations in the tail part, implying a lack of high nonlinearity in the precipitation generation processes. A gap between model and TRMM observations in the intense precipitation tail could also be explained by the simple melting treatment for graupel in our current model. All ice phase condensates melt to be rain below the subfreezing level. In addition, current convection scheme only allows vertical convection in each column and there is no slantwise convection, which might also limit impacts of riming on simulated precipitation. Moreover, a well-known technical issue with integrating the precipitating hydrometeor (snow, graupel, rain) budget equation from bottom up along the strong updrafts of convective clouds is that precipitation falling from above into the layer cannot be accounted for (see section 2.4 in Song and Zhang 2011), resulting in underestimated precipitation amount and intensity in each layer and cumulatively at the surface. The current numerical integration framework also limits the impacts of convective ice microphysics.

Spatial distributions of the frequencies of total precipitation with intensity greater than 1 mm day−1, between 1 and 20 mm day−1, and greater than 20 mm day−1 for TRMM observations and model simulations are shown in Fig. 9. The long-standing bias of “too much light precipitation and too little heavy precipitation” in GCMs is revealed in the ZM experiment, in contrast to TRMM observations. The use of SZ11 convective cloud microphysics scheme improves model simulated light (1–20 mm day−1) and heavy (>20 mm day−1) precipitation events over the Indian Ocean, Maritime Continent, SPCZ, and ITCZ, but still misses heavy precipitation over central Africa and Amazon basin. The SZ11 convective microphysics scheme with our modifications for the precipitation initiation and generation processes (SZaftu) reproduces the heavy precipitation frequencies over central Africa and Amazon basin, as seen in the TRMM observations. Thus, the heavy precipitation over Central Africa and Amazon basin is particularly sensitive to the warm rain processes in the convective cloud microphysics. Furthermore, simulated precipitation intensity frequency is insensitive to our modifications for the convective ice microphysical processes.

Fig. 9.
Fig. 9.

Spatial distributions of the frequencies of total precipitation intensity greater than 1 mm day−1, between 1 and 20 mm day−1, and greater than 20 mm day−1 for TRMM observations and model simulations.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

To further demonstrate the impact of different convective microphysics parameterizations on simulated precipitation intensities, the joint PDF of the convective available potential energy (CAPE) and convective precipitation is shown in Fig. 10. The ZM95 scheme which employs a CAPE-based trigger function to determine the convection occurrence relies heavily on CAPE. The ZM95 scheme computes the vertical profiles of mass flux and the thermodynamic properties of updrafts and downdrafts using a bulk plume method. The SZ11 convective cloud microphysics is embedded within the saturated updrafts. The temperature and moisture tendencies from convective heating/drying and vertical transport are calculated using a CAPE-based quasi-equilibrium closure.

Fig. 10.
Fig. 10.

Joint probability distribution functions of CAPE vs convective precipitation rate over the tropics (20°S–20°N) for model simulations.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

An approximately linear relationship with positive correlation between CAPE and convective precipitation in the ZM experiment (Fig. 10), consistent with the findings by Wang et al. (2021) using the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv1) in which ZM95 scheme is used. Larger CAPE corresponds to larger atmospheric instability, leading to stronger convective precipitation. The maximum convective precipitation, however, is confined below ∼60 mm day−1 regardless of CAPE values, consistent with the deficiency of heavy precipitation as seen in Fig. 8. In the SZ experiment, the most striking feature is the increased frequency occurrence of heavy precipitations (with precipitation rates higher than 60 mm day−1 for given CAPE values), which corresponds to the higher frequency of heavy convective precipitation as seen in Fig. 8. Since the CAPE threshold for triggering deep convection is set to 70 J kg−1, there is a neat boundary in the joint PDFs of CAPE and convective precipitation at the corresponding CAPE limit. Below 70 J kg−1, shallow convection mostly produces light precipitation with intensities less than ∼10 mm day−1, and above it deep convection produces stronger precipitation. Moreover, the CAPE values are generally smaller in the SZ experiment than those in the ZM experiment, as can be seen from the frequency occurrence of CAPE values > 400 J kg−1. This is because as soon as CAPE reaches the threshold of 70 J kg−1, convective microphysics produces strong convective precipitation and consequently the atmospheric instability is efficiently consumed, which prevents the atmosphere from accumulating energy to produce larger CAPE. With the introduction of the SZ11 convective microphysics, the linear relationship between CAPE and convective precipitation no longer exists, particularly for the heavy precipitation regime. The production of heavy precipitation is controlled more by the convective cloud microphysical processes (e.g., precipitation initiation and generation processes) which reduces the dependence of precipitation on CAPE even though the CAPE-based trigger function and closure are still used. This can be further demonstrated in the SZaftu experiment in which we increase the warm rain initiation and generation process rates. The heavy precipitation occurs more frequently at low CAPE values and the CAPE values are further smaller as compared to the SZ experiment. The modifications of convective ice microphysical processes have a small impact on the joint PDFs of CAPE and convective precipitation, as shown in the XReICE_EL17_rime experiment in Fig. 10.

We also show the relationship of CAPE and total precipitation over the ARM Darwin and GOAmazon sites during their intensive observing periods (IOPs) of the TWP-ICE and GOAmazon campaigns in Fig. 11. CAPE and total precipitation are from the corresponding large-scale forcing datasets (Tang et al. 2016; Xie et al. 2010). Though total precipitation is used instead of the convective precipitation due to the difficulties in partitioning precipitation types, the majority of the precipitation over these two tropical sites is predominately convective type. As shown in Fig. 11, over the Darwin and GOAmazon sites, no apparent relationship is seen between total precipitation and CAPE based on observations, somewhat resembling the SZ and SZaftu experiments with use of the explicit convective microphysics scheme. Note that the simulated CAPEs are quite different from those derived from observations, especially during TWP-ICE. The difference could be due to the potential inconsistency in the CAPE calculations with the air parcel models (e.g., parcel launch level) and/or bulk ensemble plume CAPE in model versus individual cloud scene CAPE in observation. But our focus here is more on the relationship between precipitation and CAPE, and the relationship does not depend on the methods for calculating CAPE.

Fig. 11.
Fig. 11.

Scatterplot of CAPE vs total precipitation rate at the (top) ARM site during the GOAmazon experiment (IOP1, 15 Feb–26 Mar; IOP2, 1 Sep–10 Oct 2014) and (bottom) Darwin site during the TWP-ICE experiment (19–25 Jan 2006), and from model simulations.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

Finally, we examine the climate mean precipitation. Figure 12 shows the global distribution of annual mean precipitation from GPCP and CMAP observations, as well as differences between model simulations and CMAP observations. The GPCP product is known to underestimate precipitation intensities (Kooperman et al. 2016). Overall, the geographical distributions of precipitation from model simulations broadly agree with those in observations (see Fig. S10). With the use of the SZ11 microphysics scheme, the overestimation of precipitation over ITCZ, SPCZ and western Indian Ocean is enhanced as compared to the ZM experiment. Both the ZM and SZ experiments underestimate precipitation over the Amazon basin, which is improved in the experiments with warm rain enhancement (i.e., SZaftu, XReICE_EL17, XReICE_EL17_rime, Conv_snow_detr) to be closer to observations. The triple-pattern biases over the western Indian Ocean, eastern Indian Ocean, and tropical warm pool regions are reduced in the Conv_snow_detr experiment compared to the SZ experiment. It remains unclear for the cause of the persistent overestimation of precipitation over the ITCZ in all simulations and requires further investigation. Introducing a stochastic convective parameterization into CAM5 and E3SM-EAMv1.0 further overestimates the precipitation over eastern equatorial Pacific compared to the default model (Wang and Zhang 2016; Wang et al. 2021). Neither does replacing the default deterministic convective triggering function with the new dynamic convective triggering function into CAM5 and E3SM-EAMv1.0 (Xie et al. 2019; Song and Zhang 2017; Cui et al. 2021) help with reducing the climatological mean precipitation biases over the tropics.

Fig. 12.
Fig. 12.

Global distribution of annual mean total precipitation for GPCP and CMAP observations, and differences between model simulations and CMAP observations.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0136.1

5. Discussion and conclusions

In Lin et al. (2021), we improved the convective cloud microphysics scheme by: 1) considering sedimentation of cloud ice crystals that do not fall in the original scheme and parameterizing it using the X-Re terminal velocity parameterization, and applying a new terminal velocity parameterization that depends on environmental conditions for convective snow, 2) adding a new hydrometeor category, “graupel,” to the original four-class (cloud liquid, cloud ice, rain, and snow) microphysics scheme, and 3) allowing convective clouds to detrain snow particles into stratiform cloud areas. In this study, we further modified rain initiation and generation rates in warm clouds, guided by satellite observations. We then examined the improved convective microphysics scheme in the global mode of NCAR CAM5.3 and evaluated the impacts of the improved convective cloud microphysics on simulated global climate.

In the global model simulation and testing, one significant issue associated with the convective microphysics parameterizations emerges. A direct implementation of the SZ11 convective microphysics scheme in CAM5.3 leads to a significant TOA radiation imbalance due to the excessive liquid water over the tropics and midlatitude. The latter likely results from the use of the inappropriate warm rain formation parameterizations, which was not addressed in L21. We scale up the rain initiation and generation processes by constraining the model simulated LWP and TOA radiation budget with the satellite-synergy LWP climatology (MAC-LWP product; Elsaesser et al. 2017b) and the CERES-EBAF product. The TOA radiation flux balance, mainly through the shortwave cloud forcing, is thus largely restored over the tropics and midlatitudes, along with improvements in simulated LWP climatology and tropical precipitation. Here, we call for attentions to be paid for better understanding of the convective warm rain initiation and generation, which is challenging due to a lack of observations.

CAM5.3 with the use of the SZ11 convective microphysics (the SZ experiment) overestimates the convective ice mass by more than a factor of 2 at ∼8 km and by more than a factor of ∼5 at ∼12 km and above as compared with DP 2C-ICE retrievals. In DP 2C-ICE estimates, most of the deep convective FWC is confined within the mixed-phase region (between the −37° and 0°C isotherms), whereas the model simulates the majority of FWC near the homogeneous freezing level as shown in the SZ experiment. Among our improvements to the SZ11 scheme in L21, adding graupel microphysics and the accompanying increase in ice fall speed play the most important role in reducing the convective ice overestimation in the mid and upper troposphere. This is mainly because graupel itself is characterized by significantly larger fall speeds (up to ∼6 m s−1) throughout the atmosphere, and large sedimentation of graupel at lower altitudes also prevents it from being transported upward, resulting in an overall reduction of FWC at higher altitudes. We also demonstrate that the geographic distributions of graupel occurrence frequency is well simulated over the continents; while the graupel occurrence frequency over open oceans particularly in the oceanic storm track regions remains highly uncertain due to a lack of observations.

The use of the SZ11 convective microphysics scheme (the SZ experiment) is shown to mitigate the bias of “too much light precipitation and too little heavy precipitation.” The simulated precipitation frequency is relatively insensitive to the ice processes in the convective microphysics scheme. Rather it is quite sensitive to the precipitation generation processes of liquid water conversion to rainwater (SZ versus SZaftu experiments). The SZaftu experiment further decreases the frequency of light precipitation and increases the frequency of precipitation intensity greater than 40 mm day−1 but is still not able to capture the TRMM observations in the tail part, implying a lack of the stochastic nature of precipitation (Wang and Zhang 2016) and a lack of the representation of organized convection (Moncrieff et al. 2017; Moncrieff 2019; Pendergrass 2020; Roca and Fiolleau 2020).

Convective cloud microphysics proves to be of great importance in affecting atmospheric aerosol vertical distributions through wet scavenging (Shan et al. 2021; Wang et al. 2013). Convective detrainment is closely linked to the convective microphysical properties to better simulate the impact of anvil cirrus on the climate system and its response to a changing climate. Underdeveloped convective cloud microphysics requires additional effort aiming to better represent the interactions of convection with large-scale clouds and the response of convection to aerosol perturbations (Tao et al. 2012).

Acknowledgments.

This research was supported by the U.S. Department of Energy (DOE) Atmospheric System Research (ASR) Program [Office of Science (OBER)] under Grant DE-SC0018190. The authors would like to acknowledge the use of computational resources (doi:10.5065/D6RX99HX) at the NCAR–Wyoming Supercomputing Center provided by the National Science Foundation and the state of Wyoming and supported by NCAR’s Computational and Information Systems Laboratory. We thank the editor and the anonymous reviewers for their constructive comments that helped improve the manuscript.

Data availability statement.

The Clouds and the Earth’s Radiant Energy System (CERES)–Energy Balanced and Filled (EBAF) L3b product can be accessed at https://ceres.larc.nasa.gov/data/. CloudSat–CALIPSO 2B-CLDCLASS-LIDAR and 2C-ICE data products used in this analysis were acquired from the CloudSat Data Processing Center and can be accessed at http://www.cloudsat.cira.colostate.edu. GPCP and CMAP monthly precipitation datasets are provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, from their website at https://psl.noaa.gov/data/gridded/data.gpcp.html and https://www.psl.noaa.gov/data/gridded/data.cmap.html, respectively. Tropical Rainfall Measuring Mission (TRMM) (2011), TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25° × 0.25° 3B42 V7 are provided by Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, (doi:10.5067/TRMM/TMPA/3H/7) at https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_7/summary. MAC-LWP can be accessed at https://disc.gsfc.nasa.gov/datasets/MACLWP_diurnal_1/summary?keywords=measures. DOE ARM TWP-ICE and GOAmazon large-scale forcing data can be accessed from the ARM Data Center (https://adc.arm.gov/discovery/).

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