The Summertime Precipitation Bias in E3SM Atmosphere Model Version 1 over the Central United States

Zheng, Xue; Golaz, Jean‐Christophe; Xie, Shaocheng; Tang, Qi; Lin, Wuyin; Zhang, M.; Ma, Hsi‐Yen; Roesler, Erika Louise

doi:10.1029/2019jd030662

Cited by 18 publications

(19 citation statements)

References 88 publications

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“…The NEXRAD observations used in this study reveal that EAMv1 fails to simulate the occurrence of large ice-phase particles at high levels in deep convective clouds. In addition, the conclusion that "simulated deep convection is not deep enough" also echoes the dry bias seen in GCMs as manifested in underestimations of total precipitation and individually large rain rates over the CONUS (e.g., Zheng et al, 2019). We have now shown that this model deficiency cannot be significantly improved by tuning a single value of the physical parameters in the ZM cumulus and MG2 cloud microphysics schemes.…”

Section: Conclusion and Discussionmentioning

confidence: 62%

“…As evaluated in Zheng et al (2019), E3SM v1 failed to simulate the diurnal variation of precipitation over the central US, where the observed nocturnal peak is greatly underestimated. Xie et al (2019) improved the diurnal cycle of convection in E3SM v1 recently by modifying the convective trigger function in the ZM scheme.…”

Section: Comparison Of Vertical Distribution Of Radar Reflectivitymentioning

confidence: 99%

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Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

et al. 2021

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Abstract. The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

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Section: Conclusion and Discussionmentioning

confidence: 62%

Section: Comparison Of Vertical Distribution Of Radar Reflectivitymentioning

confidence: 99%

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

et al. 2021

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“…EAMv1 was found to have difficulties in capturing the observed diurnal cycle of precipitation and propagation of mesoscale convective systems in both midlatitude and tropics, particularly the nocturnal peaks over these regions (Rasch et al, 2019; Tang, Klein, et al, 2019; Zheng et al, 2019). With the revised convective trigger for ZM, Xie et al (2019) showed that the phase of diurnal cycle of precipitation simulated by EAMv1 was significantly improved.…”

Section: Scm Testmentioning

confidence: 99%

“…As a result, model convection is triggered too easily during the day particularly over land in summer. This also partially causes the failure of capturing nocturnal convection in many models (Lee et al, 2007(Lee et al, , 2008Surcel et al, 2010;Zheng et al, 2019) since nocturnal convection is often elevated and decoupled from the surface (Geerts et al, 2017;Marsham et al, 2011;Xie et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an Improved Convective Triggering Function: Observational Evidence and SCM Tests

et al. 2020

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This study provides a strong observational support for a recently developed convective triggering function that uses the large‐scale dynamic convective available potential energy (dCAPE) as a constraint combined with an unrestricted air parcel launch level (ULL) to relax the unrealistic strong coupling of convection to surface heating and capture nocturnal elevated convection. Both case study and statistical analysis are conducted using the observations collected from the Department of Energy's Atmospheric Radiation Measurement program at its Southern Great Plains and Manaus (MAO) sites. They show that dCAPE has a much stronger correlation with precipitation than convective available potential energy, and ULL is essential to detect elevated convection above boundary layer under both midlatitude and tropical conditions and for both afternoon and nighttime deep convection regimes. Sensitivity tests with the single‐column model (SCM) of the Department of Energy's Energy Exascale Earth System Model indicate that the role of dCAPE in suppressing daytime convection is more effective for tropical convection than midlatitude convection. Even though the dCAPE can suppress the overestimated convection, ULL plays a much bigger role in improving the diurnal cycle of precipitation than dCAPE. It not only helps capture nocturnal elevated convection but also significantly removes the spurious morning precipitation seen in the default model, due to the release of unstable energy at night. However, the use of ULL has led to an overestimation of light‐to‐moderate precipitation (1–10 mm/day) due to more convection being triggered above boundary layer.

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“…Over ocean, the amplitude of the diurnal cycle is typically too weak, and the peak time does not match the observations (Dai, 2006). These problems are known to be closely related to the convection trigger function used in the convective parameterizations (e.g., Dai & Trenberth, 2004; Lee et al., 2008; Wang et al., 2020; Xie & Zhang, 2000; Xie et al., 2019; Xie, Zhang, Boyle, et al., 2004; X. Zheng et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Zhang

Lin

Vogelmann

et al. 2021

J Adv Model Earth Syst

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Convection is critical to precipitation, heat and moisture transport, cloud amount and distribution, as well as to the global energy budget (Arakawa, 2004;Arakawa & Schubert, 1974). Properly representing convection is critical to successful numerical weather prediction (NWP) and climate simulations by general circulation models (GCMs). However, because current coarse-resolution models cannot explicitly resolve convection, it must be represented with convective parameterizations. Deficiencies in these parameterizations Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large-scale environmental information associated with convective events is obtained from the longterm constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid-latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE-based triggers in terms of the F1 score metric, widely used to estimate the performance of ML methods. The site-specific ML trigger functions can achieve, respectively, 91% and 93% F1 scores at SGP and MAO. The unified trigger also has a 91% F1 score, with virtually no degradation from the site-specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE-based triggers.Plain Language Summary Deficiencies in convection trigger function, a set of conditions used to determine whether the convection will be activated at a given time in General Circulation Models (GCMs), have critical impacts on model simulated climate. This work presents a novel convection trigger function using a machine learning (ML) model. Environmental information on convective events are obtained from long-term data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites, which represent two distinct convective regimes. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The ML trigger substantially outperf...

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The Summertime Precipitation Bias in E3SM Atmosphere Model Version 1 over the Central United States

Cited by 18 publications

References 88 publications

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

Evaluation of an Improved Convective Triggering Function: Observational Evidence and SCM Tests

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

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