The field data is the Southern Great Plains of 1997 (SGP97), which includes a complex precipitation process in a large-scale model and has difficulty representing observational signals. CPs are an important process for simulating cloud systems in models that play a role in precipitation initiation and intensity. Although CP improvement studies have been published over the decades, there are still problems reproducing precipitation patterns (e.g., diurnal precipitation cycle) over ocean and land in large-scale models (Dai.
In the case of nocturnal precipitation features, the processes that induce precipitation are moisture convergence in low-level jet streams from the Gulf of Mexico (Higgins et al. 1997) and westerly winds from the lee of the Rockies (Maddox 1980). study of SCMs at SGP, Wang et al. 2015) experimented that convective inhibition of the trigger functions adopted in the Simplified Arakawa-Schubert (SAS) scheme plays an important role in simulating the nighttime peak of the diurnal cycle, compared to Zhang-Mcfalane (ZM ) scheme. In the current dissertation, 9 CPs are evaluated to investigate common features: old Kain-Fritsch (OKF; Kain and Fritsch 1990), Kain-Fritsch (KF; Kain 2004), old SAS (OSAS;.
There are two types of CPs in the evaluation: the adjustment scheme and the mass flux scheme. Previous studies have already proven the GCE performance in the case of SGP (Xu et al. compare many CRMs with each other to improve the parameterization for model communities during SGP 1997 (SGP97) in the warm season. In the same case, Lee et al. 2010) our understanding mechanism of the diurnal cycle of precipitation and cloud radiation feedback.
We used SCM forcing data in the case of the SGP in 1997 (SGP97) to validate the model simulations.
Evaluation of Convective Parametrizations for Precipitation
Chapter Ⅳ
Comparison of Vertical Profiles for Cloud and Radiation Processes
Chapter Ⅴ
Impact on Diurnal Cycle of Precipitation
Chapter Ⅵ
Summary and Concluding Remarks
On the other hand, the hydrometeors of the convection-type simulations are underpredicted than those of the grid-type. In nature, grid-type anvil clouds drop more precipitation to the surface than deep convection. In a model column, because the amounts of precipitation from grid-scale clouds are simulated quantitatively smaller than convective systems, even though the model condition is wetter.
Note that there is an unbalanced relationship of grid-typed runs between precipitation amount and cloud radiative feedback. This is the reason why all ice particles are underestimated; the weak upward transport of moisture. Almost CPs have too many cloud water particles and this is due to more humid condition.
This is because SCM conceptually assumes that anvil clouds that contribute to grid-scale rainfall have greater coverage than convective systems found in nature. But most CPs have a systematic bias in the timing of rainfall during the day, with more erroneous rainfall during the day. For the closure assumption, SAS-type schemes generate stronger upward transports than other convtection-type schemes.
In view of the trigger function, the SAS-type physics well simulates nocturnal peak due to the convective inhibition adopted in the trigger function. The ensemble is one of the promising ways to improve model performance to minimize uncertainties from the assumptions of CPs (Hawkins and Sutton 2009; Knutti et al. 2010). There are examples in these experiments that interactions between CP, MP, and RAD are diverse in clouds, and deficiencies from CPs play a role in simulating different precipitation and vertical distributions.
Although CP simulations are identical in cloud systems, deficiencies in CP assumptions result in different precipitation distributions and vertical profiles for cloud and radiative processes. A possible step to improve CP is to stabilize the convection ratio with other different schemes such as MP and RAD. Then, interactive simulations must explore the understanding of cloud precipitation and radiative processes because SCM is a non-interactive mode that cannot analyze the physical and dynamical response in large-scale model systems.
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Zeng, X., and Coauthors, 2007: Evaluation of clouds in long-term cloud-resolving model simulations with observational data. Hamilton, 2011: Improved representation of boundary layer clouds over the Southeast Pacific in ARW-WRF using a modified Tiedtke Cumulus parameterization scheme. Mcfarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Center general circulation model.
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