Xiaocong Wang

Using a single-column model, we investigate how trigger and closure treatments in ZM, TDK, and UNICON convection schemes affect the diurnal cycle of precipitation over China. For late-afternoon rainfall, TDK and ZM schemes with standard CAPE closure simulate precipitation peaks around noon, substantially earlier than observed. The UNICON scheme, with cold-pool feedback deactivated, also produces a noontime peak. For nocturnal rainfall, all schemes spuriously produce a noontime peak, in stark contrast to the observed nocturnal maximum. These behaviors are closely related to closure assumptions and the resulting cloud-base mass flux, which is primarily governed by CAPE in ZM and TDK, and responds more directly to surface fluxes in UNICON. By accounting for the imbalance between deep convection and boundary-layer production through specific modifications: adopting the dCAPE trigger in ZM, activating cold-pool feedback in UNICON, and applying the non-equilibrium closure in TDK, the simulated afternoon precipitation is delayed and in better agreement with observations. In addition, allowing elevated convection in ZM and UNICON improves nocturnal rainfall. The improved diurnal cycle is also evident in the diabatic heating fields. All modifications lead to a delayed and more pronounced upward-tilting heating structure as a result of the suppression of early-afternoon convection, with ZM and TDK extending to higher altitudes, while both intensified and extended in UNICON. These changes alleviate the upper-level cold bias in TDK and ZM. In UNICON, however, the overly strong convection reverses the upper-tropospheric bias from cold to warm with stronger magnitude, thereby worsening the bias.

Using a single‑column model, we investigate how trigger and closure treatments in ZM, TDK, and UNICON convection schemes affect DCP over China. For late-afternoon rainfall, TDK and ZM schemes with standard CAPE closure simulate precipitation peaks around noon, substantially earlier than observed. The UNICON scheme, with cold‑pool feedback deactivated, also produces a noontime peak. For nocturnal rainfall, all schemes spuriously produce a noontime peak, in stark contrast to the observed nocturnal maximum. These behaviors are closely related to closure assumptions , which is primarily governed by CAPE in ZM and TDK, and responds more directly to surface fluxes in UNICON. By accounting for the imbalance between deep convection and boundary‑layer production through specific modifications: adopting the dCAPE trigger in ZM, activating cold‑pool feedback in UNICON, and applying the non‑equilibrium closure in TDK, the simulated afternoon precipitation is delayed and in better agreement with observations. In addition, allowing elevated convection in ZM and UNICON improves nocturnal rainfall. The improved diurnal cycle is also evident in the diabatic heating fields. All modifications lead to a delayed and more pronounced upward‑tilting heating structure as a result of the suppression of early-afternoon convection, with ZM and TDK extending to higher altitudes, while both intensified and extended in UNICON. These changes alleviate the upper‑level cold bias in TDK and ZM. In UNICON, however, the overly strong convection reverses the upper‑tropospheric bias from cold to warm with stronger magnitude, thereby worsening the bias.

Using a single‑column model, we investigate how trigger and closure treatments in ZM, TDK, and UNICON convection schemes affect DCP over China. For late-afternoon rainfall, TDK and ZM schemes with standard CAPE closure simulate precipitation peaks around noon, substantially earlier than observed. The UNICON scheme, with cold‑pool feedback deactivated, also produces a noontime peak. For nocturnal rainfall, all schemes spuriously produce a noontime peak, in stark contrast to the observed nocturnal maximum. These behaviors are closely related to closure assumptions , which is primarily governed by CAPE in ZM and TDK, and responds more directly to surface fluxes in UNICON. By accounting for the imbalance between deep convection and boundary‑layer production through specific modifications: adopting the dCAPE trigger in ZM, activating cold‑pool feedback in UNICON, and applying the non‑equilibrium closure in TDK, the simulated afternoon precipitation is delayed and in better agreement with observations. In addition, allowing elevated convection in ZM and UNICON improves nocturnal rainfall. The improved diurnal cycle is also evident in the diabatic heating fields. All modifications lead to a delayed and more pronounced upward‑tilting heating structure as a result of the suppression of early-afternoon convection, with ZM and TDK extending to higher altitudes, while both intensified and extended in UNICON. These changes alleviate the upper‑level cold bias in TDK and ZM. In UNICON, however, the overly strong convection reverses the upper‑tropospheric bias from cold to warm with stronger magnitude, thereby worsening the bias.

Using a single‑column model, we investigate how trigger and closure treatments in ZM, TDK, and UNICON convection schemes affect DCP over China. For late-afternoon rainfall, TDK and ZM schemes with standard CAPE closure simulate precipitation peaks around noon, substantially earlier than observed. The UNICON scheme, with cold‑pool feedback deactivated, also produces a noontime peak. For nocturnal rainfall, all schemes spuriously produce a noontime peak, in stark contrast to the observed nocturnal maximum. These behaviors are closely related to closure assumptions , which is primarily governed by CAPE in ZM and TDK, and responds more directly to surface fluxes in UNICON. By accounting for the imbalance between deep convection and boundary‑layer production through specific modifications: adopting the dCAPE trigger in ZM, activating cold‑pool feedback in UNICON, and applying the non‑equilibrium closure in TDK, the simulated afternoon precipitation is delayed and in better agreement with observations. In addition, allowing elevated convection in ZM and UNICON improves nocturnal rainfall. The improved diurnal cycle is also evident in the diabatic heating fields. All modifications lead to a delayed and more pronounced upward‑tilting heating structure as a result of the suppression of early-afternoon convection, with ZM and TDK extending to higher altitudes, while both intensified and extended in UNICON. These changes alleviate the upper‑level cold bias in TDK and ZM. In UNICON, however, the overly strong convection reverses the upper‑tropospheric bias from cold to warm with stronger magnitude, thereby worsening the bias.

The impact of three‐dimensional (3D) shortwave radiation on cloud condensate, size and lifetime is investigated using large‐eddy simulations coupled to an interactive land surface model (LSM). Results show 3D radiation produces higher cloud tops and greater cloud condensate than 1D radiation, resulting from larger downward shortwave fluxes beneath clouds. The enhanced radiative flux leads to increased surface temperature, which in turn amplifies sensible and latent heat fluxes beneath clouds by about 40%, thereby promoting further cloud development. 3D radiation produces larger and longer‐lived clouds than 1D radiation, with the tail in size distribution occurring near 1,400 m in 3D‐LSM versus 1,100 m in 1D‐LSM. These differences arise from the contrasting surface heterogeneity induced by the 1D and 3D radiation schemes. Unlike 1D‐LSM, where cloud shadows fall directly beneath clouds and locally suppress updrafts, 3D‐LSM displaces shadows, favoring cloud‐scale circulations that provide positive feedback, leading to thicker, larger, and longer‐lived clouds.

This study investigates the impact of a scale-aware convective parameterization scheme (CPS) on the simulation of typhoon track and intensity through a series of experiments using the Global-to-Regional Integrated forecast SysTem (GRIST) model. Through a series of experiments using the GRIST model, we investigate the impact of scale-aware CPS on the simulation of typhoon track and intensity. The results of four typhoon cases show that scale-aware CPS helps to reduce the track error by about 15 km and the intensity error by about 10%, demonstrating the benefits of scale-aware CPS on typhoon modeling. The Lekima case is then used as an example to illustrate the reasons behind the improved typhoon intensity with scale-aware CPS. By analyzing the budget equation of surface pressure tendency contributed by different physical processes, we found the pressure depression due to CPS heating is about 0.6 hPa h-1 weaker when scale-aware CPS is applied. However, the microphysics process takes up the convective instability left over by CPS and outweighs the reduction in parameterized convection, yielding a net pressure depression of about 1 hPa h-1. This suggests the suppression of sub-grid convection favors the stimulation of stronger microphysics heating due to stronger grid-scale ascending. Further examination of the inner-core precipitation validates the assertion. Indeed, when the scale-aware CPS is applied, the microphysics precipitation and corresponding diabatic heating in the inner-core region increase by about 9.5% and 13.8% respectively, which more than offsets the decrease in precipitation and diabatic heating of the CPS component. This is accompanied by stronger grid-scale ascending and accelerated radial winds, with the low-level inflow and upper-level outflow increased by about 2 m s-1. In summary, by suppressing sub-grid convection and enhancing microphysics process, the scale-aware CPS intensifies the secondary circulation, producing stronger diabatic heating and thus enhanced typhoon intensity.

We conduct a process‑oriented analysis of summertime diurnal cycle of precipitation (DCP) over China by comparing three widely used reanalyses (ERA5, JRA‑55, and MERRA‑2) with satellite observations. While all reanalyses capture the observed nocturnal precipitation peak related to elevated convection, they differ in simulating the daytime rainfall timing. JRA-55 and MERRA-2 better capture the observed timing, whereas ERA5 exhibits a 3-hour phase advance. The superior performance of JRA-55 is attributed to its gradual development of deep convection, supported by sustained heating and convective eddy transport. In contrast, ERA5 develops deep convection too rapidly, resulting in premature peaks in heating and precipitation. MERRA‑2 also produces early‑peaking convective rainfall, but with notably weaker intensity, suggesting that its better diurnal cycle is achieved largely through the suppression of convective precipitation. Diurnal cloud structures further corroborate these differences. Whereas JRA‑55 exhibits a slowly developing, upward‑tilting cloud structure from morning to afternoon, ERA5 and MERRA-2 peak earlier and have a shorter duration. The role of large‑scale forcing, quantified by CAPE and dynamic CAPE (dCAPE), is further tied to the performance of the convection schemes. Results show the peak timing of dCAPE lags that of CAPE and aligns more closely with the observed precipitation. While convective precipitation in ERA5 and MERRA‑2 tracks CAPE more closely, in JRA‑55 it aligns better with dCAPE, thereby yielding a more realistic DCP. This contrast highlights the critical influence of triggering choice on cumulus convection.

We conduct a process‑oriented analysis of summertime diurnal cycle of precipitation (DCP) over China by comparing three widely used reanalyses (ERA5, JRA‑55, and MERRA‑2) with satellite observations. While all reanalyses capture the observed nocturnal precipitation peak related to elevated convection, they differ in simulating the daytime rainfall timing. JRA-55 and MERRA-2 better capture the observed timing, whereas ERA5 exhibits a 3-hour phase advance. The superior performance of JRA-55 is attributed to its gradual development of deep convection, supported by sustained heating and convective eddy transport. In contrast, ERA5 develops deep convection too rapidly, resulting in premature peaks in heating and precipitation. MERRA‑2 also produces early‑peaking convective rainfall, but with notably weaker intensity, suggesting that its better diurnal cycle is achieved largely through the suppression of convective precipitation. Diurnal cloud structures further corroborate these differences. Whereas JRA‑55 exhibits a slowly developing, upward‑tilting cloud structure from morning to afternoon, ERA5 and MERRA-2 peak earlier and have a shorter duration. The role of large‑scale forcing, quantified by CAPE and dynamic CAPE (dCAPE), is further tied to the performance of the convection schemes. Results show the peak timing of dCAPE lags that of CAPE and aligns more closely with the observed precipitation. While convective precipitation in ERA5 and MERRA‑2 tracks CAPE more closely, in JRA‑55 it aligns better with dCAPE, thereby yielding a more realistic DCP. This contrast highlights the critical influence of triggering choice on cumulus convection.

We conduct a process‑oriented analysis of summertime diurnal cycle of precipitation (DCP) over China by comparing three widely used reanalyses (ERA5, JRA‑55, and MERRA‑2) with satellite observations. While all reanalyses capture the observed nocturnal precipitation peak related to elevated convection, they differ in simulating the daytime rainfall timing. JRA-55 and MERRA-2 better capture the observed timing, whereas ERA5 exhibits a 3-hour phase advance. The superior performance of JRA-55 is attributed to its gradual development of deep convection, supported by sustained heating and convective eddy transport. In contrast, ERA5 develops deep convection too rapidly, resulting in premature peaks in heating and precipitation. MERRA‑2 also produces early‑peaking convective rainfall, but with notably weaker intensity, suggesting that its better diurnal cycle is achieved largely through the suppression of convective precipitation. Diurnal cloud structures further corroborate these differences. Whereas JRA‑55 exhibits a slowly developing, upward‑tilting cloud structure from morning to afternoon, ERA5 and MERRA-2 peak earlier and have a shorter duration. The role of large‑scale forcing, quantified by CAPE and dynamic CAPE (dCAPE), is further tied to the performance of the convection schemes. Results show the peak timing of dCAPE lags that of CAPE and aligns more closely with the observed precipitation. While convective precipitation in ERA5 and MERRA‑2 tracks CAPE more closely, in JRA‑55 it aligns better with dCAPE, thereby yielding a more realistic DCP. This contrast highlights the critical influence of triggering choice on cumulus convection.

Leveraging observational data from the TRMM‐KWAJEX field campaign, which captures the evolution of mixed Rossby‐gravity (MRG) waves, this study evaluates how parameterization schemes influence convectively coupled MRG waves in the Community Atmospheric Model single‐column framework (SCAM). Four configurations are examined: the traditional approach that uses separate deep (ZM) and shallow (UW) convection schemes (CAM_ZMUW), unified shallow‐deep convection (CAM_UNICON), unified cloud‐turbulence (CAM_CLUBB), and super‐parameterization (CAM_SP). While precipitation is relatively insensitive to parameterization choice, diabatic heating fields differ markedly, which shape temperature, moisture and MRG wave characteristics. All simulations capture the general eastward tilt of MRG waves, but with varying fidelity. CAM_ZMUW and CAM_UNICON produce overly strong convection, while CAM_CLUBB underestimates low‐level early‐phase moistening. Only CAM_CLUBB and CAM_SP reproduce the successive vertical subgrid‐scale transport of moist static energy convergence, though the transport is weaker in CAM_CLUBB, whereas mass‐flux‐based schemes lack coherent vertical organization. Experiments combining Cloud Layers Unified by Binormals (CLUBB) with ZM deep convection behave similarly to CAM_ZMUW, indicating strong sensitivity to deep‐convection scheme. Applying stricter convective triggering in ZM convection alleviates the lower‐tropospheric dry bias but does not lead to an improved simulation of the convectively coupled MRG wave. The stochastic Subgrid Importance Latin Hypercube Sampler, a Monte Carlo technique designed to integrate over subgrid variability and improve estimates of its effects on microphysical process rates, has a relatively minor impact on MRG modeling in CLUBB. These results underscore the need to improve the representation of shallow‐to‐deep convective transitions in mass‐flux schemes and to enhance the vertical subgrid‐scale transport within the CLUBB framework.