Abstract. This article investigates how variations in aerosol loading affect droplet activation and the production of warm rain in shallow cumulus clouds. It is also examined whether the resulting differences can be identified and quantified using 35 GHz Ka-band radar signatures and how these relationships depend on the cloud life-cycle stage. Three idealized large-eddy simulations, T13 (clean, aerosol number concentration Nₐ = 100 cm−³), T53 (intermediate, Nₐ = 1000 cm−³), and T73 (polluted, Nₐ = 5000 cm−³), coupled to a Lagrangian cloud model (LES–LCM), are conducted with a fixed aerosol size-distribution shape under identical thermodynamic conditions based on the Barbados Oceanographic and Meteorological Experiment (BOMEX). Persistent particle tracking is used to reconstruct Lagrangian histories of super-droplets. Analysis of droplet activation and growth statistics shows that clusters of relative humidity (RH) values distinguish core-like and entrained-shell-like activation pathways. The simulation results further show that increased aerosol loading reduces the temporal window available for droplet activation, spectral broadening, and collision–coalescence. Accordingly, approximately 20.1 %, 15.3 %, and 15.0 % of trajectories reach drizzle size (r ≥ 40 µm) in the clean, intermediate, and polluted cases, respectively. Mixing diagnostics shift toward more deactivation-dominated behavior with increasing aerosol loading. These findings suggest that aerosol-dependent microphysical pathways remain detectable in Ka-band Doppler radar signatures. Therefore, Ka-band radar observations provide an observationally testable signature of delayed activation and suppressed warm-rain production, while it shall be noted that knowledge about the evolution state of the cloud system is essential for drawing conclusions about aerosol effects.

Submesoscale turbulence influences phytoplankton growth by enhancing upper-ocean stratification, nutrient entrainment, and horizontal tracer variability. However, the specific mechanisms and their relative contributions under convective forcing remain unclear. Here, we use large-eddy simulations coupled with a Lagrangian plankton model to quantify the physical and biological impacts of submesoscale turbulence across varying cooling intensities and nutrient levels, comparing cases with and without submesoscale turbulence. Under weak cooling with low nutrients, submesoscale turbulence enhances nutrient entrainment but initially suppresses growth by subducting phytoplankton into deeper, light-limited waters. As restratification progresses, greater surface retention yields net growth enhancement alongside higher nutrient supply. Conversely, when nutrients are abundant, submesoscale turbulence enhances light exposure and promotes growth. Under strong cooling conditions, convective mixing dominates, homogenizing tracers and diminishing submesoscale influence. Nonethelss, weak restratification from submesoscale turbulence still yields small gains in light exposure and growth. Additionally, horizontal heterogeneity suppresses growth when cooling is weak and nutrients are low, but is negligible otherwise. These findings highlight the need to represent interacting physical processes in ocean models. Simplified parameterizations that omit these interactions may misrepresent biological responses, especially under weak cooling.

Understanding warm rain initiation through droplet collision and coalescence is a fundamental yet complex challenge in cloud microphysics. Although it is well-known that sufficiently large droplets, so-called precipitation embryos (PEs), may accelerate droplet collisions, it is uncertain how many and how large these PEs should be to affect rain initiation substantially. We address this question using an ensemble of box simulations with Lagrangian cloud microphysics. We find that warm rain initiation is substantially accelerated only if the PE size or number (or the product of those) exceeds a critical threshold necessary to compensate for the PE-induced suppression of collisions among non-PEs. The sensitivity of this threshold to the shape of the droplet size distribution and turbulence effects on the collision process is analysed. It is shown that more and larger PEs are needed to accelerate rain initiation when collisions are already efficient without PEs, e.g. due to a broad droplet size distribution or a strong turbulence effect. Beyond increasing our fundamental understanding of the precipitation process in warm clouds, our results may help to constrain the effect of PE-like particles intentionally or unintentionally added in climate intervention approaches, such as rain enhancement or marine cloud brightening.

The mixing of reactive tracers within the ocean mixed layer, phytoplankton transported downwards from the sea surface and nutrients transported upwards from the mixed layer depth (MLD), is investigated using large eddy simulation coupled to a Lagrangian plankton model. The study focuses on how vertical and horizontal heterogeneity in tracer distribution is generated and how it influences an autumn phytoplankton bloom. The vertical gradient appears in the profiles of horizontal mean phytoplankton and nutrient concentrations, P and N, and it reduces phytoplankton production by photosynthesis compared to the cases with uniform distributions. The reduction ratio decreases as the mixed-layer mean N increases, but it remains relatively insensitive to other conditions such as MLD, surface forcing, stratification below the mixed layer, and the initial N. Phytoplankton and nutrient concentrations show a negative correlation in the horizontal plane, which becomes stronger with increasing depth. Its contribution to plankton production by photosynthesis is negligible, however, because the correlation is weak near the sea surface and the reaction time scale is much longer than the turbulent mixing time scale. It is also found that the vertical gradients of P and N are smaller, and the negative correlation is stronger in the convective mixed layer than in the shear-driven mixed layer. A simple box plankton model, which takes into account the mixing process of tracers, is proposed and used to investigate how mixing affects the prediction of an autumn phytoplankton bloom.

The activation of aerosol particles and its contribution to cloud droplet size distributions in shallow cumulus clouds are investigated using a Lagrangian cloud model (LCM), allowing explicit simulations of the activation and tracking of each simulated hydrometeor. This analysis is focused on the differences in activations for aerosol particles following central updraft from a cloud base (CB) and particles laterally entrained (LE) aloft. Strong supersaturation fluctuations induced by entrainment and turbulent mixing at cloud edges cause most LE particles to be deactivated soon after activation, substantially limiting their lifetime and growth relative to CB particles. Most net activation—the difference between activation and deactivation—occurs at the cloud base and vanishes aloft, thus making the cloud droplet concentration vertically uniform above the cloud base. Time series of various variables following individual particles confirm the distinctive nature of CB and LE activation. Activation spectra follow the theoretical prediction at high supersaturation conditions, but their magnitudes are smaller because of particle deactivation. At low supersaturation conditions, the spectra deviate from the theoretical prediction since they are affected by the transport of the CB particles activated elsewhere.

Langmuir circulation (LC) plays an important role in deepening the mixed layer, especially when the ocean is weakly stratified. LC parameterization has been known to improve the accuracy of the mixed-layer depth, sea surface temperature, and ocean ventilation in the Southern Ocean. However, changes in ocean dynamics and biogeochemical processes owing to LC mixing have rarely been investigated. In this study, we implemented LC parameterization to a physical–biogeochemical coupled model and examined the changes in water properties and circulation and their effects on biogeochemical processes in the Southern Ocean. The LC effect enhances the turbulent mixing length scale and turbulent kinetic energy, especially in the subantarctic region, resulting in the deepening of the mixed layer. Increased vertical mixing causes deeper warm and saline water to reach the surface layer and weakens the surface meridional velocity by transferring momentum deeper. The weakening of the meridional velocity decreases equatorward freshwater transport, causing the surface water to become saltier and denser north of 50°S adjacent to the formation site of Subantarctic Mode Water, which enhances ocean ventilation eventually. Additionally, the weak equatorward velocity drives the retreat of sea-ice south of 60°S, leading to cooler and fresher water in the southern region of 60°S. Changes in the sea-ice distribution, mixed-layer depth, and dissolved inorganic carbon distribution alter the air-sea CO2 exchange, suggesting that the LC parameterization in the model can ameliorate 10% of the air-sea CO2 exchange. The LC effects on biogeochemical tracers, such as iron (Fe) and dissolved inorganic carbon, are mainly determined by changes in ocean circulation, and the modest improvement in primary productivity is mainly attributed to the changes in the Fe distribution in the high-nutrient, low-chlorophyll region, where Fe availability limits primary production. Although the direct LC effects occur near the surface, the altered meridional circulation spreads the effects to a deeper layer and improves the overall representation of physical, biogeochemical tracers and air-sea CO2 exchange in the Southern Ocean, suggesting that a simple LC parameterization can improve the ocean model.

Sparse observations in the East/Japan Sea (EJS) suggested that open-ocean deep convection occurs south of Vladivostok; however, more recent observations suggest that deep convection occurs along the continental slope, resulting in bottom water formation in the EJS. We investigated the process of deep convection along the EJS continental slope using large-eddy simulation (LES), which demonstrated that dense water, formed by strong wintertime cooling in the shelf, flows down along the slope as a bottom Ekman current. The characteristics of the initial dense water were relatively well conserved on the continental slope during convection, but they changed rapidly by mixing with the surrounding waters in the open ocean. Accordingly, slope convection penetrated deeper compared to open-ocean convection under the same surface heat flux. Our numerical experiments showed that, under typical surface cooling during winter (i.e., 200 W m –2 ), slope convection reaches depths greater than 2,700 m, generating a potential ventilation process for deep- and bottom-water formations, whereas open-ocean convection reaches approximately 700 m depth, contributing to the intermediate- and central-water formations in the EJS. Various topography experiments revealed that downward speed was proportional to the continental-slope inclination; the initial characteristics remained relatively well conserved at a small continental-slope inclination. Increased salinity due to brine rejection in the shelf could accelerate the slope convection.

The ocean mixed layer model (OMLM) is improved using the large-eddy simulation (LES) and the inverse estimation method. A comparison of OMLM (Noh model) and LES results reveals that underestimation of the turbulent kinetic energy (TKE) flux in the OMLM causes a negative bias of the mixed layer depth (MLD) during convection, when the wind stress is weak or the latitude is high. It is further found that the entrainment layer thickness is underestimated. The effects of alternative approaches of parameterizations in the OMLM, such as nonlocal mixing, length scales, Prandtl number, and TKE flux, are examined with an aim to reduce the bias. Simultaneous optimizations of empirical constants in the various versions of Noh model with different parameterization options are then carried out via an iterative Green's function approach with LES data as constraining data. An improved OMLM is obtained, which reflects various new features, including the enhanced TKE flux, and the new model is found to improve the performance in all cases, namely, wind-mixing, surface heating, and surface cooling cases. The effect of the OMLMgrid resolution on the optimal empirical constants is also investigated.

The roles of the drop size distribution (DSD) and turbulence in the autoconversion rate A are investigated by analyzing Lagrangian cloud model (LCM) data for shallow cumulus clouds. The correlations of DSD and turbulence with other cloud parameters are estimated, and they are applied to parameterize their effects on A. A new parameterization of A based on this analysis is proposed: A=αqc7/3Nc−1/3HRc−Rc0 $A=\alpha {q}_{c}^{7/3}{N}_{c}^{-1/3}H\left({R}_{c}-{R}_{c0}\right)$ with α=aNc−XRc−Rc0(1+bε) $\alpha =a{N}_{c}^{-X}\left({R}_{c}-{R}_{c0}\right)(1+b\varepsilon )$ , where qc ${q}_{c}$ is the cloud water mixing ratio, Nc ${N}_{c}$ and Rc ${R}_{c}$ are the number concentration and the volume mean radius of cloud droplets, ε $\varepsilon $ is the dissipation rate, Rc0 ${R}_{c0}$ is the threshold value of Rc ${R}_{c}$ , H is the Heaviside step function, and X, a $a$ , and b are constants. Here, Nc−XRc−Rc0 ${N}_{c}^{-X}\left({R}_{c}-{R}_{c0}\right)$ represents the effect of DSD via its correlation with Nc ${N}_{c}$ and Rc ${R}_{c}$ , while A∝qc7/3Nc−1/3 $A\propto {q}_{c}^{7/3}{N}_{c}^{-1/3}$ represents the effect of gravitational collisional growth for a given DSD and turbulence. The correlation between turbulence and DSD makes b larger than expected from turbulence‐induced collision enhancement only. The effects of DSD and turbulence and their correlations with qc ${q}_{c}$ and Nc ${N}_{c}$ explain a wide range of exponent values of qc ${q}_{c}$ and Nc ${N}_{c}$ in many existing parameterizations of A. The new parameterization is compared with the LCM data and applied to a bulk cloud model (BCM) while clarifying the difference between the cloud droplet mixing processes in the LCM and BCM. The importance of DSD and turbulence in raindrop formation in shallow cumulus clouds is shown by comparing the A results with and without these effects.

A Lagrangian plankton model (LPM) is developed, in which the motion of a large number of Lagrangian particles, representing a parcel of plankton, is calculated under the turbulence field simulated by large-eddy simulation. A spring phytoplankton bloom is realized using the LPM, and the mechanism for its 1 generation is investigated. The criterion based on these results is proposed as z(c)(-2)<[1/(C-1 delta(E))(2)+1/(C-2 delta(S))(2)], where delta(E) (= u(*)/f) is the scale for the Ekman boundary layer, delta(S) (=u(*)(2)/(fQ(0))(1/2)) is the scale for the depth of a seasonal thermocline, u(*) is the wind stress, Q(0) is the surface buoyancy flux, f is the Coriolis parameter, and C-1 and C-2 are constants. The critical depth hypothesis can be applied for the onset of a spring bloom, when(C-2 delta(S)/C-1 delta(E))(2)<<1, using the mixing layer depth instead of the mixed layer depth, as z(c) > C-2 delta(S), but the critical turbulence hypothesis can be applied, as z(c) > C-1 delta(E), when(C-2 delta S/C-1 delta E)(2)>>1. A spring bloom is more likely to occur at higher atitudes, even if the atmospheric forcing is the same. The diurnal variation of Q(0) tends to increase the strength of the spring bloom at small u(*). Furthermore, various statistics of Lagrangian particles, such as the mixing length of plankton, the residence time of plankton within the euphotic zone, and the growth of plankton clarify the movement and growth of plankton cells. Plain Language Summary Phytoplankton concentrations increase rapidly in early spring in the high-latitude ocean, which is known as a spring phytoplankton bloom. It is caused by the weakened vertical mixing after surface cooling in winter switches to surface heating, which makes plankton spend more time near the surface, where the sunlight for photosynthesis is plentiful. In order to understand this phenomenon and predict its occurrence, a new type of plankton model is developed, in which a large number of particles, representing a parcel of plankton, move around in turbulent flows of the ocean. The spring bloom is simulated by the plankton model. The simulations show that a spring bloom occurs when the depth of a seasonal thermocline is shallower than the critical depth determined by radiation penetration. It can also occur in the absence of surface heating, when wind is very weak. Furthermore, results show that spring blooms are more likely to occur at higher latitudes, even if the atmospheric condition is the same. A new criterion for the onset of the spring bloom is proposed. Various statistical analyses of plankton particles are carried out to illustrate how plankton move and grow at different latitudes under the influence of the diurnal variation.

In our presentation, we will show the performance of a new earth system model developed at the Korea Institute of Ocean Science and Technology (KIOST), called the KIOST-ESM. The KIOST-ESM is based on a low-resolution version of the Geophysical Fluid Dynamics Laboratory Climate Model version 2.5. The main changes made to the base model include using new cumulus convection and ocean mixed layer parameterization schemes, which improve the model fidelity significantly. In addition, the KIOST-ESM adopts dynamic vegetation and new soil respiration schemes in its land model component. The performance of the KIOST-ESM was assessed in pre-industrial and historical simulations that are made as part of its participation into Climate Model Intercomparison Project phase 6. The response of the earth system to increases in greenhouse gas concentrations were analyzed in the ScenarioMIP simulations. The KIOST-ESM exhibited superior performance compared to the base model in terms of the mean sea surface temperature over the Southern Ocean and over the cold tongue in the tropical Pacific. The KIOST-ESM can also simulate the dominant tropical variability in the intraseasonal (Madden-Julian Oscillation) and interannual (El Niño-Southern Oscillation) timescales more realistically than the base model. On the other hand, like many other contemporary ESMs, the KIOST-ESM showed notable cold bias in the Northern Hemisphere, and the so-called double-Intertropical Convergence Zone bias remains. The ScenarioMIP results confirm the global average surface atmospheric temperature responds to the CO2 concentration.

The roughness sublayer (RSL) is one compartment of the surface layer (SL) where turbulence deviates from Monin–Obukhov similarity theory. As the computing power increases, model grid sizes approach the gray zone of turbulence in the energy-containing range and the lowest model layer is located within the RSL. From this perspective, the RSL has an important implication in atmospheric modeling research. However, it has not been explicitly simulated in atmospheric mesoscale models. This study incorporates the RSL model proposed by Harman and Finnigan (2007, 2008) into the Jiménez et al. (2012) SL scheme. A high-resolution simulation performed with the Weather Research and Forecasting model (WRF) illustrates the impacts of the RSL parameterization on the wind, air temperature, and rainfall simulation in the atmospheric boundary layer. As the roughness parameters vary with the atmospheric stability and vegetative phenology in the RSL model, our RSL implementation reproduces the observed surface wind, particularly over tall canopies in the winter season by reducing the root mean square error (RMSE) from 3.1 to 1.8 m s−1. Moreover, the improvement is relevant to air temperature (from 2.74 to 2.67 K of RMSE) and precipitation (from 140 to 135 mm per month of RMSE). Our findings suggest that the RSL must be properly considered both for better weather and climate simulations and for the application of wind energy and atmospheric dispersion.

The source codes and namelist files are prepared to reproduce the results of the study entitled 'Hampering Effect of Wave-Driven Turbulence on Advection Speed and Diffusion Rate of Pollutants: A Large Eddy Simulation Study'. Download the originial source codes for the Parallelized LES model (PALM) at https://palm.muk.uni-hannover.de and use the source codes provided here as 'user-defined code' in PALM. The data can be used to reproduce the figures of the scientific article named after the study.

The 9th International Workshop on Modeling the Ocean (IWMO 2017) was held in the modern campus of

These are the large eddy simulation data which are used for comparing the boundary layers of the atmosphere and the ocean during convection and investigating their latitudinal dependence.The data are obtained by using PALM (https://palm.muk.uni-hannover.de/trac) . The configuration of Langmuir circulation is the same as in Noh et al. (2011). The simulations are conducted with two different latitudes (10N, 40N). The period of integration is twice the inertial time scale of each latitude. Constant wind stress (u*=0~0.02m/s) or geostrophic wind(Ug=0~20m/s) is applied in ocean and atmosphere simulations. The data representing the characteristics of the boundary layers (ex. horizontal velocity, potential temperature, boundary layer depth(height), vertical buoyancy flux, bulk Richarson number, vertical turbulent kinetic energy etc.) are available in netCDF format.Most of the simulations have been carried out on the supercomputer system supported by the National Center for Meteorological Supercomputer of Korea Meteorological Administration (KMA).

The boundary layers of the atmosphere and the ocean are compared during convection, and their latitudinal dependence is investigated. The results are applied to examine the parameterization of the boundary layer depth in the K-profile parameterization (KPP) model. The bulk Richardson numberRi(b)varies excessively with time in the high-latitude (HL) oceanic boundary layer (OBL) without unresolved shearVt2, as a result of inertial oscillation. Inclusion ofVt2is also necessary in the atmospheric boundary layer (ABL) to mitigate the large variation ofRi(b)with wind stress. Stratification and velocity shear near the boundary layer height/depth are stronger and thicker at low latitudes (LLs), where the Ekman length scale is larger and the inertial time scale is longer. This enhanced shear makes the entrainment buoyancy flux larger at LL. Analysis of the turbulent kinetic energy (TKE) budget in the entrainment zone is carried out to explain the variation ofRi(b)depending on the boundary layer and the latitude. This analysis shows that the TKE production in the entrainment zone is dominated by shear production at LL, but by the TKE flux at HL, and thatVt2represents the contribution from the TKE flux to the entrainment zone. The enhancement of vertical TKE by Langmuir circulation (LC) does not depend on the latitude, but it decreases with depth faster at LL. The result suggests that the parameterization of the boundary layer depth in the KPP model should be different depending on whether it is the ABL or the OBL and depending on the latitude.

A curious phenomenon found in phytoplankton communities is the forming of socalled thin layers, wherein phytoplankton biomass can stretch out kilometres in the horizontal but only a few metres in the vertical. These layers are typically found at the pycnocline, just below the surface mixed layer. Thin layers are usually attributed to a range of complex environmental and species-dependent factors. However, we believe that, given the frequency at which this phenomenon is observed, a simpler mechanism is at play. In this study, we found that phytoplankton thin layers can be attributed simply to a decreasing light availability with depth, when there is an abundance of nutrients in the euphotic zone and below the mixed layer. This mechanism was ascertained using a number of modelling approaches ranging in complexity from analytical solutions of a simple 1-dimensional plankton model to a 3-dimensional biophysical model incorporating large-eddy simulation. The conditions which, according to the results of our study, allow thin layers to form are ubiquitous in the coastal ocean and are therefore a likely candidate explanation as to why planktonic thin layers are so frequently observed.

Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at > 3.5 μm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.

This study investigates how pycnocline smoothing and subgrid-scale variability of density profiles influence the determination of the mixed layer depth (MLD) in the global ocean, and applies the results of analysis to assess the ability of ocean general circulation models (OGCM) to simulate the MLD. For this purpose, individual, monthly mean, and climatological profiles are analyzed over a horizontal resolution of 1 degrees x 1 degrees for both observation data (Argo) and eddy-resolving OGCM (OFES) results. It is found that the MLDs from averaged profiles are generally smaller than those from individual profiles because of pycnocline smoothing induced by the averaging process. A correlation is found between the decrease in MLD Delta h and the increase in pycnocline thickness Delta delta of averaged profiles, except during winter in the high-latitude ocean. The relation is estimated as Delta h = -alpha Delta delta - beta, where alpha similar or equal to 0.7 in all cases, but beta increases with the subgrid-scale variability of density profiles. A correlation is also found between Delta h and the standard deviation of the MLD within a grid. The results are applied to estimate how much of the MLD bias of OFES is due to prediction error and how much is due to profile error, induced by different pycnocline smoothing and the subgrid-scale variability of density profiles. The study also shows how profile error varies with the threshold density difference criterion.