Evaluation of high‐level clouds in cloud resolving model simulations with ARM and KWAJEX observations

Abstract

In this study, we evaluate high-level clouds in a cloud resolving model during two convective cases, ARM9707 and KWAJEX. The simulated joint histograms of cloud occurrence and radar reflectivity compare well with cloud radar and satellite observations when using a two-moment microphysics scheme. However, simulations performed with a single moment microphysical scheme exhibit low biases of approximately 20 dB. During convective events, two-moment microphysical overestimate the amount of high-level cloud and one-moment microphysics precipitate too readily and underestimate the amount and height of high-level cloud. For ARM9707, persistent large positive biases in high-level cloud are found, which are not sensitive to changes in ice particle fall velocity and ice nuclei number concentration in the two-moment microphysics. These biases are caused by biases in large-scale forcing and maintained by the periodic lateral boundary conditions. The combined effects include significant biases in high-level cloud amount, radiation, and high sensitivity of cloud amount to nudging time scale in both convective cases. The high sensitivity of high-level cloud amount to the thermodynamic nudging time scale suggests that thermodynamic nudging can be a powerful ''tuning'' parameter for the simulated cloud and radiation but should be applied with caution. The role of the periodic lateral boundary conditions in reinforcing the biases in cloud and radiation suggests that reducing the uncertainty in the large-scale forcing in high levels is important for similar convective cases and has far reaching implications for simulating high-level clouds in super-parameterized global climate models such as the multiscale modeling framework.

The diurnal cycle of clouds and precipitation at the ARM SGP site: Cloud radar observations and simulations from the multiscale modeling framework

“…The overprediction of high cloud and excessive upper level humidity (as we also find here) has been recognized in other MMF studies [ McFarlane et al ., ; Zhang et al ., ]. It has been suggested that this is related to the overestimation of vertical transport of humidity [ Zhang et al ., ], the adoption of periodic lateral boundary conditions in the cloud‐resolved model embedded in MMF [ Luo and Stephens , ; Liu et al ., ], and problems with the parameterization of the ice cloud microphysics [ Khairoutdinov et al ., ].…”

Effects of Explicit Convection on Land Surface Air Temperature and Land‐Atmosphere Coupling in the Thermal Feedback Pathway

“…In recent work (Sun & Pritchard, ) we showed that the use of Superparameterization (SP) in the SP version of the Community Atmosphere Model (SPCAM), which uses an embedded cloud resolving model within each GCM grid column to replace typical parameterizations of moist convection and vertical mixing, simulates more realistically damped coupling signals in the water pathway of the land‐atmosphere feedback loop compared to its counterpart (i.e., regular Community Atmosphere Model). Some studies examined the effects of superparameterization on cloud properties and shown that in some regimes SPCAM can offer improvements in cloud cover (Marchand & Ackerman, ) and high‐level cloud amount (Liu et al, ), albeit to different degrees depending on model configuration or microphysics scheme. Such cloud properties directly affect atmospheric radiative transfer and thus the energy budget at the land surface.…”

Environmental controls on isolated convection in the Amazon: an observational and numerical modeling study

“…The only variable that exhibited a noticeable sensitivity to the microphysics scheme was the column water vapor, where the P3 scheme demonstrated a stronger correlation with the observations. Compared with previous work addressing the model sensitivity to microphysics, model domain, and resolution (KHAIROUTDINOV; RANDALL, 2003;KHAIROUTDINOV;RANDALL, 2006;BLOSSEY et al, 2007;KHAIROUTDINOV et al, 2009;HENDERSON;PINCUS, 2009;LIU;MUHLBAUER;ACKERMAN, 2015;BLOSSEY;MOHRMANN, 2021), this thesis focused specifically on the Amazon region and considered the calculation of surface fluxes through the SLM. Although the recent work by GONÇALVES et al (2022) employed the GoAmazon2014/5 observations, they did not assess the evolution of moisture content and surface latent and sensible heat fluxes.…”