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It is challenging to parameterize subgrid vertical momentum fluxes in marine shallow cumulus layers that contain a jet in the profile of horizontal wind. In a large‐eddy simulation of such a layer, it is found that the momentum flux in the direction of strongest wind magnitude has a three‐layer structure. The lowest layer, from the ocean surface up to the jet maximum, has downgradient momentum flux. The middle layer, from the jet maximum up to an altitude several hundred meters above, has upgradient (i.e., countergradient) momentum flux because of transport of low‐magnitude momentum upward through the jet maximum. In the upper layer, the layer‐average momentum flux is weak. The budget of momentum flux shows that in the middle and upper layers, both the buoyancy production term and turbulent advection (i.e., third‐order flux‐of‐flux) terms are important. To parameterize the profile of momentum flux in a single‐column model, the momentum flux is prognosed in this study. The buoyancy production and flux‐of‐flux terms are parameterized by integrating them over a subgrid probability density function with an assumed normal‐mixture shape. The resulting parameterized fluxes and mean‐wind profiles are demonstrated to be comparable to those produced in large‐eddy simulations, both for two marine shallow cumulus cases with upgradient fluxes and for a continental cumulus case and two stratocumulus cases with downgradient fluxes. In the two marine shallow cumulus cases, the parameterization is able to capture the upgradient momentum flux above the jet maximum and the weak momentum fluxes aloft. Plain Language Summary A fine‐scale simulation of a shallow cumulus cloud layer has been performed, and it exhibits a three‐layer structure of turbulent momentum flux in the vertical. This structure is difficult to approximate in coarse‐resolution global atmospheric models, but a method for doing so is proposed and tested. Key Points A simulation of shallow cumuli exhibits three layers, with different behavior of momentum flux in each The middle layer exhibits upgradient momentum flux, and the upper layer has weak momentum flux This three‐layer structure is parameterized by a higher‐order model closed with an assumed PDF

It is difficult for coarse‐resolution global models of the atmosphere to accurately simulate the observed distribution of low clouds. In particular, it is difficult for moist turbulence closure models to simulate sufficiently bright near‐coastal stratocumulus (Sc) without simulating overly bright marine shallow cumuli (Cu). To parameterize bright Sc, a turbulence parameterization must damp the turbulent fluxes of heat and moisture above cloud top in order to prevent excessive entrainment of dry air into cloud top. To parameterize dim shallow Cu, the subgrid variances of temperature and moisture must remain large, in order to permit partial cloudiness. However, damping the fluxes but not the variances just above cloud top is difficult if a parameterization uses a single “master” time scale to damp both. In nature, the above‐cloud fluxes are damped by pressure fluctuations, whereas scalar variances are damped by a different process, namely, turbulent dissipation. In a stably stratified inversion above cloud, pressure damping is large but turbulent dissipation is small. To avoid this problem, a multitime scale parameterization for damping has been developed. The damping parameterization has been implemented in a global model and evaluated. The parameterization is capable of dimming shallow Cu while producing adequately bright Sc. Plain Language Summary This paper describes an assumption in turbulence modeling that is known to be questionable but is still sometimes made. To avoid making this assumption, the formulation of a particular turbulence model is generalized. The generalized formulation, when implemented in a global model of the atmosphere, changes the pattern of low‐altitude clouds. Key Points Some turbulence parameterizations use a single master turbulence length/time scale even though this assumption is an oversimplification This paper parameterizes turbulence by use of multiple turbulent time scales, including one for dissipation and another for pressure Use of the new multiscale parameterization in a global model significantly alters the distribution of stratocumulus and shallow cumulus clouds

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing. Plain Language Summary The U.S. Department of Energy recently released version two of its Energy Exascale Earth System Model (E3SM). E3SMv2 experienced a significant evolution in many of its model components (most notably the atmosphere and sea ice models), and its supporting software infrastructure. In this work, we document the computational performance of E3SMv2 and analyze its ability to reproduce the observed climate. To accomplish this, we utilize the standard Diagnosis and Evaluation and Characterization of Klima experiments augmented with historical simulations for the period 1850–2015. We find that E3SMv2 is nearly twice as fast as its predecessor and more accurately reproduces the observed climate in a number of metrics, most notably clouds and precipitation. We also find that the model's simulated response to increasing carbon dioxide (the equilibrium climate sensitivity) is much more realistic. Unfortunately, E3SMv2 underestimates the global mean surface temperature compared to observations during the second half of historical period. Using sensitivity experiments, where forcing agents (carbon dioxide, aerosols) are selectively disabled in the model, we determine that correcting this problem would require a strong reduction in the impact of aerosols. Key Points E3SMv2 is nearly twice as fast as E3SMv1 with a simulated climate that is improved in many metrics (e.g., precipitation and clouds) Climate sensitivity is substantially lower with a more plausible equilibrium climate sensitivity of 4.0 K (compared to an unlikely value of 5.3 K in E3SMv1) E3SMv2 underestimates the warming in the late historical period due to excessive aerosol‐related forcing

Every cloud parameterization contains structural model errors. The source of these errors is difficult to pinpoint because cloud parameterizations contain nonlinearities and feedbacks. To elucidate these model inadequacies, this paper uses a general-purpose ensemble parameter estimation technique. In principle, the technique is applicable to any parameterization that contains a number of adjustable coefficients. It optimizes or calibrates parameter values by attempting to match predicted fields to reference datasets. Rather than striving to find the single best set of parameter values, the output is instead an ensemble of parameter sets. This ensemble provides a wealth of information. In particular, it can help uncover model deficiencies and structural errors that might not otherwise be easily revealed. The calibration technique is applied to an existing single-column model (SCM) that parameterizes boundary layer clouds. The SCM is a higher-order turbulence closure model. It is closed using a multivariate probability density function (PDF) that represents subgrid-scale variability. Reference datasets are provided by large-eddy simulations (LES) of a variety of cloudy boundary layers. The calibration technique locates some model errors in the SCM. As a result, empirical modifications are suggested. These modifications are evaluated with independent datasets and found to lead to an overall improvement in the SCM’s performance.

Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In many cloud microphysics schemes, the warm rain non‐linear process rates calculated based on grid‐mean cloud properties are usually scaled by an enhancement factor (EF) to account for the effects of subgrid cloud variability. In our study, we find that the EF derived from Cloud Layers Unified by Binormals in Community Atmosphere Model version 6 (CAM6) is severely overestimated in most of the cloudy oceanic areas, which leads to strong overestimation of the autoconversion rate. We improve the EF in warm rain simulation by developing a new formula for in‐cloud subgrid cloud water variance. With the updated subgrid cloud water variance and EF treatment, the liquid cloud fraction (LCF) and cloud optical thickness (COT) increases noticeably for marine stratocumulus, and the shortwave cloud forcing (SWCF) matches better with observations. The updated formula improves the relationship between autoconversion rate and cloud droplet number concentration, and it decreases the sensitivity of autoconversion rate to aerosols. The sensitivity of liquid water path to aerosols decreases noticeably and is in better agreement with that in MODIS. Although the sensitivity of COT is similar to that in MODIS, CAM6 underestimates the sensitivity of grid‐mean SWCF to aerosols because of the underestimation in the sensitivities of LCF and in‐cloud SWCF. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol‐cloud interaction in GCMs. Plain Language Summary Restricted by the coarse horizontal resolution, the global climate model needs to scale the warm rain non‐linear process rates with an enhancement factor to account for the effects of subgrid variability of cloud water. However, this variability is severely overestimated in most of the cloudy oceanic areas in Community Atmosphere Model version 6, which leads to a strong overestimation of the precipitation. We develop a new formula for calculating the subgrid variability of cloud water, which greatly improves the magnitude of the variability and contributes to better simulation of cloud properties and shortwave cloud forcing (SWCF) using satellite observation as a benchmark. The sensitivity of cloud liquid water path to aerosols decreases noticeably with the new formula. The model underestimates the sensitivity of SWCF to aerosols due to the underestimation in the sensitivities of liquid cloud fraction and cloud albedo. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol‐cloud interaction in the climate model. Key Points A new formula for the in‐cloud subgrid cloud water variance is developed to improve the enhancement factor of autoconversion rate The simulated sensitivity of cloud liquid water path to aerosols decreases obviously with the updated formula The simulated sensitivity of grid‐mean shortwave cloud forcing to aerosols is underestimated compared to MODIS observation

Microphysical processes, such as the formation, growth, and evaporation of precipitation, interact with variability and covariances (e.g., fluxes) in moisture and heat content. For instance, evaporation of rain may produce cold pools, which in turn may trigger fresh convection and precipitation. These effects are usually omitted or else crudely parameterized at subgrid scales in weather and climate models.A more formal approach is pursued here, based on predictive, horizontally averaged equations for the variances, covariances, and fluxes of moisture and heat content. These higher-order moment equations contain microphysical source terms. The microphysics terms can be integrated analytically, given a suitably simple warm-rain microphysics scheme and an approximate assumption about the multivariate distribution of cloud-related and precipitation-related variables. Performing the integrations provides exact expressions within an idealized context.A large-eddy simulation (LES) of a shallow precipitating cumulus case is performed here, and it indicates that the microphysical effects on (co)variances and fluxes can be large. In some budgets and altitude ranges, they are dominant terms. The analytic expressions for the integrals are implemented in a single-column, higher-order closure model. Interactive single-column simulations agree qualitatively with the LES. The analytic integrations form a parameterization of microphysical effects in their own right, and they also serve as benchmark solutions that can be compared to non-analytic integration methods.

The subgrid-scale representation of hydrometeor fields is important for calculating microphysical process rates. In order to represent subgrid-scale variability, the Cloud Layers Unified By Binormals (CLUBB) parameterization uses a multivariate probability density function (PDF). In addition to vertical velocity, temperature, and moisture fields, the PDF includes hydrometeor fields. Previously, hydrometeor fields were assumed to follow a multivariate single lognormal distribution. Now, in order to better represent the distribution of hydrometeors, two new multivariate PDFs are formulated and introduced.The new PDFs represent hydrometeors using either a delta-lognormal or a delta-double-lognormal shape. The two new PDF distributions, plus the previous single lognormal shape, are compared to histograms of data taken from large-eddy simulations (LESs) of a precipitating cumulus case, a drizzling stratocumulus case, and a deep convective case. Finally, the warm microphysical process rates produced by the different hydrometeor PDFs are compared to the same process rates produced by the LES.

This paper describes the atmospheric component of the US Department of Energy's Energy Exascale Earth System Model (E3SM) version 3. Significant updates have been made to the atmospheric physics compared to earlier versions. Specifically, interactive gas chemistry has been implemented, along with improved representations of aerosols and dust emissions. A new stratiform cloud microphysics scheme more physically treats ice processes and aerosol‐cloud interactions. The deep convection parameterization has been largely improved with sophisticated microphysics for convective clouds, making model convection sensitive to large‐scale dynamics, and incorporating the dynamical and physical effects of organized mesoscale convection. Improvements in aerosol wet removal processes and parameter re‐tuning of key aerosol and cloud processes have improved model aerosol radiative forcing. The model's vertical resolution has increased from 72 to 80 layers with the extra eight layers added in the lower stratosphere to better simulate the Quasi‐Biennial Oscillation. These improvements have enhanced E3SM's capability to couple aerosol, chemistry, and biogeochemistry and reduced some long‐standing biases in simulating tropical variability. Compared to its predecessors, the model shows a much stronger signal for the Madden‐Julian Oscillation, Kelvin waves, mixed Rossby‐gravity waves, and eastward inertia‐gravity waves. Aerosol radiative forcing has been considerably reduced and is now better aligned with community best estimates, leading to significantly improved skill in simulating historical temperature records. Its simulated mean‐state climate is largely comparable to E3SMv2, but with some notable degradation in shortwave cloud radiative effect, precipitable water, and surface wind stress, which will be addressed in future updates. Plain Language Summary This study is part of a series describing the newly released version 3 of the US Department of Energy's Energy Exascale Earth System Model (E3SMv3), focusing on updates to its atmospheric component model (EAMv3). Substantial improvements have been made in representing atmospheric chemistry, aerosols, clouds, convective processes, and their interactions in the model. The model's vertical resolution in the lower stratosphere has increased to better simulate the Quasi‐Biennial Oscillation. These updates strengthen E3SM's ability to model aerosol, chemistry, and biogeochemistry, and reduce biases in tropical variability. The model now shows stronger signals for phenomena like the Madden‐Julian Oscillation and Kelvin waves. Aerosol radiative forcing is better aligned with community estimates, improving the model's skill in simulating historical temperatures. The model's simulated mean‐state climate is largely comparable to its predecessor model EAMv2. Key Points Significant updates were made to Earth System Model version 3 atmospheric physics, including gas phase chemistry, aerosols, clouds, and convection Improved cloud, convection, and vertical resolution largely improved tropical variability simulation in troposphere and stratosphere Improved aerosol representation and aerosol‐cloud interactions have led to a much‐reduced and realistic aerosol radiative forcing

An updated 2016 echocardiographic algorithm for diagnosing left ventricular (LV) diastolic dysfunction (DD) was recently proposed. We aimed to assess the reliability of the 2016 echocardiographic LVDD grading algorithm in predicting elevated LV filling pressure and clinical outcomes compared to the 2009 version. We retrospectively identified 460 consecutive patients without atrial fibrillation or significant mitral valve disease who underwent transthoracic echocardiography within 24 hours of elective heart catheterization. LV end-diastolic pressure (LVEDP) and the time constant of isovolumic pressure decay (Tau) were determined. The association between DD grading by 2009 LVDD Recommendations and 2016 Recommendations with hemodynamic parameters and all-cause mortality were compared. The 2009 LVDD Recommendations classified 55 patients (12%) as having normal, 132 (29%) as grade 1, 156 (34%) as grade 2, and 117 (25%) as grade 3 DD. Based on 2016 Recommendations, 177 patients (38%) were normal, 50 (11%) were indeterminate, 124 (27%) patients were grade 1, 75 (16%) were grade 2, 26 (6%) were grade 3 DD, and 8 (2%) were cannot determine. The 2016 Recommendations had superior discriminatory accuracy in predicting LVEDP (P<.001) but were not superior in predicting Tau. During median follow-up of 416 days (interquartile range: 5 to 2004 days), 54 patients (12%) died. Significant DD by 2016 Recommendations was associated with higher risk of mortality (P=.039, subdistribution HR1.85 [95% CI, 1.03-3.33]) in multivariable competing risk regression. The grading algorithm proposed by the 2016 LV diastolic dysfunction Recommendations detects elevated LVEDP and poor prognosis better than the 2009 Recommendations.

Ischemic mitral regurgitation (IMR) has been associated with worse outcome myocardial infarction. However, severity of mitral regurgitation (MR) and its impact on patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI) remains unknown. We sought to determine impact of increasing severity of IMR on outcomes in patients with STEMI undergoing primary PCI. All patients presenting with STEMI who underwent primary PCI within 12 hours of symptoms from 1994 to 2014 were included. IMR was graded from 0 to 4+ within 3 days of index myocardial infarction by echocardiography. Overall, 4,005 patients with STEMI were included. None, 1+, 2+, 3+, and 4+ MR were present in 3,200 (79.9%), 427 (10.7%), 260 (6.5%), 91 (2.3%), and 27 (0.7%) patients, respectively. On multivariate logistic regression analysis, more severe MR was associated with older age, female gender, lower body mass index, anemia, inferior STEMI, and longer door-to-balloon time. The 30-day mortality rates were 6.8%, 7.3%, 8.8%, 19.8%, and 26.1%, respectively, with increasing grade of MR. The 1-year mortality rates were 10.8%, 12.4%, 20.8%, 37.4%, and 37.1%, whereas 5-year mortality rates were 16.2%, 23.1%, 36.5%, 53.8%, and 63%, respectively (p <0.001 all), for none to 4+ MR. After adjusting for age, gender, co-morbidities, ejection fraction, and shock by multivariate analysis, severity of IMR was associated with incremental effect on long-term mortality (hazard ratios of 1.42, 1.83, 2.41, and 2.95 for 1+ to 4+ MR respectively, p <0.01 for all). In conclusion, higher grades of MR in patients with STEMI undergoing primary PCI are associated with worse short- and long-term outcomes.