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Abstract. In the current global climate models (GCMs), the nonlinearity effect ofsubgrid cloud variations on the parameterization of warm-rain process, e.g.,the autoconversion rate, is often treated by multiplying the resolved-scalewarm-rain process rates by a so-called enhancement factor (EF). In thisstudy, we investigate the subgrid-scale horizontal variations andcovariation of cloud water content (qc) and cloud droplet numberconcentration (Nc) in marine boundary layer (MBL) clouds based on thein situ measurements from a recent field campaign and study the implicationsfor the autoconversion rate EF in GCMs. Based on a few carefully selectedcases from the field campaign, we found that in contrast to the enhancingeffect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in asuppressing effect that tends to make EF < 1. This effect isespecially strong at cloud top, where the qc and Nc correlation canbe as high as 0.95. We also found that the physically complete EF thataccounts for the covariation of qc and Nc is significantly smallerthan its counterpart that accounts only for the subgrid variation ofqc, especially at cloud top. Although this study is based on limitedcases, it suggests that the subgrid variations of Nc and itscorrelation with qc both need to be considered for an accuratesimulation of the autoconversion process in GCMs. 

Nudging is a ubiquitous capability of numerical weather and climate models that is widely used in a variety of applications (e.g., crude data assimilation, “intelligent” interpolation between analysis times, constraining flow in tracer advection/diffusion simulations). Here, the focus is on the momentum nudging tendencies themselves, rather than the atmospheric state that results from application of the method. The initial intent was to interpret these tendencies as a quantitative estimate of model error (net parameterization error in particular). However, it was found that nudging tendencies depend strongly on the nudging time scale chosen, which is the primary result presented here. Reducing the nudging time scale reduces the difference between the model state and the target state, but much less so than the reduction in the nudging time scale, resulting in increased nudging tendencies. The dynamical core, in particular, appears to increasingly oppose nudging tendencies as the nudging time scale is reduced. A heuristic analysis suggests such a result should be expected as long as the state the model is trying to achieve differs from the target state, regardless of the type of target state (e.g., a reanalysis, another model). These results suggest nudging tendencies cannot bequantitativelyinterpreted as model error. Still, two experiments aimed at seeing how nudging can identify a withheld parameterization suggest nudging tendencies do contain some information on model errors and/or missing physical processes and still might be useful in model development and tuning, even if only qualitatively.

 

Abstract. One of the challenges inrepresenting warm rain processes in global climate models (GCMs) is relatedto the representation of the subgrid variability of cloud properties, such ascloud water and cloud droplet number concentration (CDNC), and the effectthereof on individual precipitation processes such as autoconversion. Thiseffect is conventionally treated by multiplying the resolved-scale warm rainprocess rates by an enhancement factor (E_q) which is derived fromintegrating over an assumed subgrid cloud water distribution. The assumedsubgrid cloud distribution remains highly uncertain. In this study, we derivethe subgrid variations of liquid-phase cloud properties over the tropicalocean using the satellite remote sensing products from Moderate ResolutionImaging Spectroradiometer (MODIS) and investigate the correspondingenhancement factors for the GCM parameterization of autoconversion rate. Wefind that the conventional approach of using only subgrid variability ofcloud water is insufficient and that the subgrid variability of CDNC, as wellas the correlation between the two, is also important for correctlysimulating the autoconversion process in GCMs. Using the MODIS data whichhave near-global data coverage, we find that E_q shows a strongdependence on cloud regimes due to the fact that the subgrid variability ofcloud water and CDNC is regime dependent. Our analysis shows a significantincrease of E_q from the stratocumulus (Sc) to cumulus (Cu) regions.Furthermore, the enhancement factor E_N due to the subgrid variation ofCDNC is derived from satellite observation for the first time, and resultsreveal several regions downwind of biomass burning aerosols (e.g., Gulf ofGuinea, east coast of South Africa), air pollution (i.e., East China Sea),and active volcanos (e.g., Kilauea, Hawaii, and Ambae, Vanuatu), where theE_N is comparable to or even larger than E_q, suggesting an importantrole of aerosol in influencing the E_N. MODIS observations suggest thatthe subgrid variations of cloud liquid water path (LWP) and CDNC aregenerally positively correlated. As a result, the combined enhancementfactor, including the effect of LWP and CDNC correlation, is significantlysmaller than the simple product of E_q⋅E_N. Given the importanceof warm rain processes in understanding the Earth's system dynamics and watercycle, we conclude that more observational studies are needed to provide abetter constraint on the warm rain processes in GCMs.

 

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.

 

In this study, a higher‐order closure scheme known as Cloud Layers Unified By Binormals (CLUBB) is coupled with a cloud top radiative cooling scheme (RAD). The cloud top radiative cooling scheme treats the buoyancy flux generated near the top of the boundary layer which helps the CLUBB scheme to better represent the radiation‐turbulence interaction on the condition of coarse vertical resolution. CLUBB with RAD is found to improve subtropical low‐cloud simulations, and the improvement is particularly evident for nocturnal stratocumulus. The improvements are caused by the stronger and more symmetric vertical turbulent mixing in the boundary layer, as CLUBB with RAD increases the variance of vertical velocity and vertical turbulent transports and reduces the skewness of vertical velocity by enhancing the radiative cooling effects and buoyancy fluxes at the cloud layer. The pumping effect related to the stronger vertical turbulent transports further cools and dries the lower boundary layer, which increases the local surface heating fluxes and further improves the low‐cloud simulations.

 

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.