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The parameterization of subgrid‐scale processes such as boundary layer (PBL) turbulence introduces uncertainty in Earth System Model (ESM) results. This uncertainty can contribute to or exacerbate existing biases in representing key physical processes. This study analyzes the influence of tunable parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBBX) scheme. CLUBB is the operational PBL parameterization in the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community ESM version 2 (CESM2). We perform the Morris one‐at‐a‐time (MOAT) parameter sensitivity analysis using short‐term (3‐day), initialized hindcasts of CAM6‐CLUBBX with 24 unique initial conditions. Several input parameters modulating vertical momentum flux appear most influential for various regionally‐averaged quantities, namely surface stress and shortwave cloud forcing (SWCF). These parameter sensitivities have a spatial dependence, with parameters governing momentum flux most influential in regions of high vertical wind shear (e.g., the mid‐latitude storm tracks). We next evaluate several experimental 20‐year simulations of CAM6‐CLUBBX with targeted parameter perturbations. We find that parameter perturbations produce similar physical mechanisms in both short‐term and long‐term simulations, but these physical responses can be muted due to nonlinear feedbacks manifesting over time scales longer than 3 days, thus causing differences in how output metrics respond in the long‐term simulations. Analysis of turbulent fluxes in CLUBBX indicates that the influential parameters affect vertical fluxes of heat, moisture, and momentum, providing physical pathways for the sensitivities identified in this study. 

The higher‐order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models. This study investigates an underexplored aspect of CLUBB: its capacity to simulate near‐surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux. Using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB's stability‐driven substepping performed at each atmospheric time step. The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB‐computed zonal and meridional wind speed tendencies. Our 30‐year present‐day climate simulations (1980–2010) show that static coupling overestimates 10‐m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10‐m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind‐speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near‐surface wind speeds and associated turbulent momentum transport structure. Notably, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns. 

Recent studies have demonstrated that high-resolution (∼25 km) Earth System Models (ESMs) have the potential to skillfully predict tropical cyclone (TC) occurrence and intensity. However, biases in ESM TCs still exist, largely due to the need to parameterize processes such as boundary layer (PBL) turbulence. Building on past studies, we hypothesize that the depiction of the TC PBL in ESMs is sensitive to the configuration of the PBL parameterization scheme, and that the targeted perturbation of tunable parameters can reduce biases. The Morris one-at-a-time (MOAT) method is implemented to assess the sensitivity of the TC PBL to tunable parameters in the PBL scheme in an idealized configuration of the Community Atmosphere Model, version 6 (CAM6). The MOAT method objectively identifies several parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBB) scheme that appreciably influence the structure of the TC PBL. We then perturb the parameters identified by the MOAT method within a suite of CAM6 ensemble simulations and find a reduction in model biases compared to observations and a high-resolution, cloud-resolving model. We demonstrate that the high-sensitivity parameters are tied to PBL processes that reduce turbulent mixing and effective eddy diffusivity, and that in CAM6 these parameters alter the TC PBL in a manner consistent with past modeling studies. In this way, we provide an initial identification of process-based input parameters that, when altered, have the potential to improve TC predictions by ESMs. 

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. 

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. 

Abstract 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.