Single-column Model Simulations Research Articles

A Case Study Investigating the Low Summertime CAPE Behavior in the Global Forecast System

Abstract Convective available potential energy (CAPE) is an important index for storm forecasting. Recent versions (v15.2 and v16) of the Global Forecast System (GFS) predict lower values of CAPE during summertime in the continental United States than analysis and observation. We conducted an evaluation of the GFS in simulating summertime CAPE using an example from the Unified Forecast System Case Study collection to investigate the factors that lead to the low CAPE bias in GFS. Specifically, we investigated the surface energy budget, soil properties, and near-surface and upper-level meteorological fields. Results show that the GFS simulates smaller surface latent heat flux and larger surface sensible heat flux than the observations. This can be attributed to the slightly drier-than-observed soil moisture in the GFS that comes from an offline global land data assimilation system. The lower simulated CAPE in GFS v16 is related to the early drop of surface net radiation with excessive boundary layer cloud after midday when compared with GFS v15.2. A moisture-budget analysis indicates that errors in the large-scale advection of water vapor does not contribute to the dry bias in the GFS at low levels. Common Community Physics Package single-column model (SCM) experiments suggest that with realistic initial vertical profiles, SCM simulations generate a larger CAPE than runs with GFS IC. SCM runs with an active LSM tend to produce smaller CAPE than that with prescribed surface fluxes. Note that the findings are only applicable to this case study. Including more warm-season cases would enhance the generalizability of our findings. Significance Statement Convective available potential energy (CAPE) is one of the key parameters for severe weather analysis. The low bias of CAPE is identified by forecasters as one of the key issues for the NOAA operational global numerical weather prediction model, Global Forecast System (GFS). Our case study shows that the lower CAPE in GFS is related to the drier atmosphere than observed within the lowest 1 km. Further investigations suggest that it is related to the drier atmosphere that already exists in the initial conditions, which are produced by the Global Data Assimilation System, in which an earlier 6-h GFS forecast is combined with current observations. It is also attributed to the slightly lower simulated soil moisture than observed. The lower CAPE in GFS v16 when compared with GFS v15.2 in the case analyzed here is related to excessive boundary layer cloud formation beginning at midday that leads to a drop of net radiation reaching the surface and thus less latent heat feeding back to the low-level atmosphere.

North American Monsoon Impacts Southern California's Coastal Low Clouds

AbstractLow‐level stratiform clouds modulate California's coastal climate during the warm season. Previous work describing the seasonal and daily variability of coastal low cloudiness (CLC) suggests that in July, August, and September southern California's CLC is under the influence of an additional driver, which has less impact in northern California. In this work, we introduce the link in which free‐tropospheric moisture dictated by North American Monsoon (NAM) processes can impact southern California CLC. We use in situ and remote sensing observations, as well as reanalysis and single column model simulations to identify and investigate this previously missing component. We find that monsoonal moisture advected by southeasterly flow from the core NAM region into southern California reduces CLC by diminishing cloud‐top longwave cooling. To add to an already complex brew of known factors influencing coastal cloudiness, another one is hereby introduced and should be accounted for in future work.

Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling

Abstract. As a unified weather-forecast–climate model system, Global-to-Regional Integrated forecast SysTem (GRIST-A22.7.28) currently employs two separate physics suites for weather forecast and typical long-term climate simulation, respectively. Previous AMIP-style experiments have suggested that the weather (PhysW) and climate (PhysC) physics suites, when coupled to a common dynamical core, lead to different behaviors in terms of modeling clouds and precipitation. To explore the source of their discrepancies, this study compares the two suites using a single-column model (SCM). The SCM simulations demonstrate significant differences in the simulated precipitation and low clouds. Convective parameterization is found to be a key factor responsible for these differences. Compared with PhysC, parameterized convection of PhysW plays a more important role in moisture transport and rainfall formation. The convective parameterization of PhysW also better captures the onset and retreat of rainfall events, but stronger upward moisture transport largely decreases the tropical low clouds in PhysW. These features are in tune with the previous 3D AMIP simulations. Over the typical stratus-to-stratocumulus transition regime such as the Californian coast, turbulence in PhysW is weaker than that in PhysC, and shallow convection is more prone to be triggered and leads to larger ventilation above the cloud layer, reducing stratocumulus clouds there. These two suites also have intrinsic differences in the interaction between cloud microphysics and other processes, resulting in different time step sensitivities. PhysC tends to generate more stratiform clouds with decreasing time step. This is caused by separate treatment of stratiform cloud condensation and other microphysical processes, leading to a tight interaction between macrophysics and boundary layer turbulence. In PhysW, all the microphysical processes are executed at the same temporal scale, and thus no such time step sensitivity was found.

The sensitivity of landfast sea ice to atmospheric forcing in single-column model simulations: a case study at Zhongshan Station, Antarctica

Abstract. Single-column sea ice models are used to focus on the thermodynamic evolution of the ice. Generally, these models are forced by atmospheric reanalysis in the absence of atmospheric in situ observations. Here we assess the sea ice thickness simulated by a single-column model (ICEPACK) with in situ observations obtained off Zhongshan Station for the austral winter of 2016. In the reanalysis, the surface air temperature is about 1 ∘C lower, the total precipitation is about 2 mm d−1 greater, and the surface wind speed is about 2 m s−1 higher compared to the in situ observations. We designed sensitivity experiments to evaluate the simulation bias in sea ice thickness due to the uncertainty in the individual atmospheric forcing variables. Our results show that the unrealistic precipitation in the reanalysis leads to a bias of 14.5 cm in sea ice thickness and 17.3 cm in snow depth. In addition, our data show that increasing snow depth works to gradually inhibit the growth of sea ice associated with thermal blanketing by the snow due to changing the vertical heat flux. Conversely, given suitable conditions, the sea ice thickness may grow suddenly when the snow load gives rise to flooding and leads to snow-ice formation. However, there are still uncertainties related to the model results because superimposed ice and snowdrift are not implemented in the version of the ice model used and because snow-ice formation might be overestimated at locations with landfast sea ice.

An Urban Scheme for the ECMWF Integrated Forecasting System: Single‐Column and Global Offline Application

AbstractThe societal benefits of numerical weather prediction (NWP) forecasts are most evident in populated areas. An urban representation within NWP models should provide improved forecast accuracy. Here, we present the preliminary implementation of an urban scheme within the Integrated Forecasting System (IFS) using a simplified single‐layer urban canopy model. The scheme makes assumptions of canyon geometry and considers fluxes from roads, walls, and roofs. Temperature observations were used to optimize single‐column model (SCM) parameters using the Gauss‐Newton method. Observation comparisons over six European cities, show a 2‐m temperature root‐mean‐squared error reduction from 1.85 to 1.75 K with the urban scheme. Optimized parameters were used globally at kilometric scale in a land surface model. A sensitivity experiment assuming a 100% urban world showed spatially averaged northern hemisphere 2‐m temperatures increased by 0.54 K (January) and 0.42 K (July) at night caused by changes in the albedo, emissivity, roughness, and thermal and hydrological properties. Global ∼1‐km resolution simulations using ancillary urban mapping information produce an urban heat island effect over major and minor conurbations. Only major conurbations were well represented at ∼9‐km resolution. Results from SCM simulations show a heightening of the planetary boundary layer over city sites, with the largest enhancements occurring at night in July (84 ± 48 m) caused by an increased sensible heat flux. These initial developments show the importance of a high‐resolution urban representation within NWP models. Improved parameterization and mapping will enable an online representation of energy, water, and trace gas fluxes over residential areas.

Impacts of secondary ice production on Arctic mixed-phase clouds based on ARM observations and CAM6 single-column model simulations

Abstract. For decades, measured ice crystal number concentrations have been found to be orders of magnitude higher than measured ice-nucleating particle number concentrations in moderately cold clouds. This observed discrepancy reveals the existence of secondary ice production (SIP) in addition to the primary ice nucleation. However, the importance of SIP relative to primary ice nucleation remains highly unclear. Furthermore, most weather and climate models do not represent SIP processes well, leading to large biases in simulated cloud properties. This study demonstrates a first attempt to represent different SIP mechanisms (frozen raindrop shattering, ice–ice collisional breakup, and rime splintering) in a global climate model (GCM). The model is run in the single column mode to facilitate comparisons with the Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We show the important role of SIP in four types of clouds during M-PACE (i.e., multilayer, single-layer stratus, transition, and frontal clouds), with the maximum enhancement in ice crystal number concentrations up to 4 orders of magnitude in moderately supercooled clouds. We reveal that SIP is the dominant source of ice crystals near the cloud base for the long-lived Arctic single-layer mixed-phase clouds. The model with SIP improves the occurrence and phase partitioning of the mixed-phase clouds, reverses the vertical distribution pattern of ice number concentrations, and provides a better agreement with observations. The findings of this study highlight the importance of considering SIP in GCMs.

Single Column Model Simulations of Icing Conditions in Northern Sweden: Sensitivity to Surface Model Land Use Representation

In-cloud ice mass accretion on wind turbines is a common challenge that is faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests—including the cloud water response to surface roughness and albedo effects that are related to them—must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and, to the present date, not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME three-dimensional (3D) NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insolation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In autumn cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.

Evaluation of nitrogen oxides (NO<sub><i>x</i></sub>) sources and sinks and ozone production in Colombia and surrounding areas

Abstract. In Colombia, industrialization and a shift towards intensified agriculture have led to increased emissions of air pollutants. However, the baseline state of air quality in Colombia is relatively unknown. In this study we aim to assess the baseline state of air quality in Colombia with a focus on the spatial and temporal variability in emissions and atmospheric burden of nitrogen oxides (NOx = NO + NO2) and evaluate surface NOx, ozone (O3) and carbon monoxide (CO) mixing ratios. We quantify the magnitude and spatial distribution of the four major NOx sources (lightning, anthropogenic activities, soil biogenic emissions and biomass burning) by integrating global NOx emission inventories into the mesoscale meteorology and atmospheric chemistry model, namely Weather Research and Forecasting (WRF) coupled with Chemistry (collectively WRF-Chem), at a similar resolution (∼25 km) to the Emission Database for Global Atmospheric Research (EDGAR) anthropogenic emission inventory and the Ozone Monitoring Instrument (OMI) remote sensing observations. The model indicates the largest contribution by lightning emissions (1258 Gg N yr−1), even after already significantly reducing the emissions, followed by anthropogenic (933 Gg N yr−1), soil biogenic (187 Gg N yr−1) and biomass burning emissions (104 Gg N yr−1). The comparison with OMI remote sensing observations indicated a mean bias of tropospheric NO2 columns over the whole domain (WRF-Chem minus OMI) of 0.02 (90 % CI: [−0.43, 0.70]) ×1015 molecules cm−2, which is <5 % of the mean column. However, the simulated NO2 columns are overestimated and underestimated in regions where lightning and biomass burning emissions dominate, respectively. WRF-Chem was unable to capture NOx and CO urban pollutant mixing ratios, neither in timing nor in magnitude. Yet, WRF-Chem was able to simulate the urban diurnal cycle of O3 satisfactorily but with a systematic overestimation of 10 parts per billion (ppb) due to the equally large underestimation of NO mixing ratios and, consequently, titration. This indicates that these city environments are in the NOx-saturated regime with frequent O3 titration. We conducted sensitivity experiments with an online meteorology–chemistry single-column model (SCM) to evaluate how WRF-Chem subgrid-scale-enhanced emissions could explain an improved representation of the observed O3, CO and NOx diurnal cycles. Interestingly, the SCM simulation, showing especially a shallower nocturnal inversion layer, results in a better representation of the observed diurnal cycle of urban pollutant mixing ratios without an enhancement in emissions. This stresses that, besides application of higher-resolution emission inventories and model experiments, the diurnal cycle in boundary layer dynamics (and advection) should be critically evaluated in models such as WRF-Chem to assess urban air quality. Overall, we present a concise method to quantify air quality in regions with limited surface measurements by integrating in situ and remote sensing observations. This study identifies four distinctly different source regions and shows their interannual and seasonal variability during the last 1.5 decades. It serves as a base to assess scenarios of future air quality in Colombia or similar regions with contrasting emission regimes, complex terrain and a limited air quality monitoring network.

Humidity observations and column simulations for a warm period at the Mars Phoenix lander site: Constraining the adsorptive properties of regolith

Two recalibrated sets of Phoenix (PHX) near-surface TECP air humidity measurements were compared with results from adsorptive single column model simulations during a warm clear-sky polar midsummer period, PHX sols 50–60. The model's 2 m temperatures were close to the observed values. Relative humidity (RH) is very low during the day but at night RH at 2 m reaches nearly 100% by the Zent et al. (2016) recalibration (Z), and 60–70% by the Fischer et al. (2019) recalibration (F). Model values of RH2m are close to Z and F at night and to F during the day. All three imply low water vapor pressures near the surface at night, 0.03–0.05 Pa, with a rapid increase each morning to 0.3–1 Pa and a decrease in the evening by both F and the model simulation. The model's daily adsorbed and desorbed water is in balance for regolith porosity of 16% (instead of 35% for lower latitudes). The depleted layer of nighttime air moisture extends to only about 200 m above the surface; hence the model's precipitable water content stays around the observed ~30 μm throughout the sol. The model's moisture cycle is not sensitive to tortuosity of the regolith but the in-pore molecular diffusivity should be at least 5 cm2/s for fair agreement with the observations. In the adsorption experiments there is no fog and just a hint of ground frost, as observed during this period. Strong night frosts appear if adsorption is made weak or absent in the model.

Formation of Arctic Stratocumuli Through Atmospheric Radiative Cooling

AbstractStratocumulus clouds are important to the Arctic climate because they are prevalent and exert a strong radiative forcing on the surface. However, relatively little is known about how stratocumulus clouds form in the Arctic. In this study, radiative transfer calculations are used to show that the timescale over which stably stratified Arctic temperature and water vapor profiles cool to saturation is less than typical residence times for individual air parcels in the Arctic. This result is consistent with previous studies in suggesting that elevated stratocumulus can form naturally through clear‐sky radiative cooling during all seasons, without assistance from frontal lifting or other atmospheric forcing. Single column model simulations of the cloud formation process, after radiative cooling has resulted in saturation in a stably stratified profile, suggest that stratocumulus cloud properties are sensitive to the characteristics of the environment in which the formation process takes place. For example, sensitivity tests suggest that clouds may attain liquid water paths of over 50 g/m2 if they form in moist environments but may become locked in a low‐liquid water path quasi steady state or dissipate within hours if they form in dry environments. A potential consequence of these sensitivities is that when an Arctic stratocumulus layer forms by radiative cooling, it is more likely to become optically thick, optically thin, or dissipate than it is to obtain an intermediate optical thickness. This could help explain why the cloudy and radiatively clear atmospheric states are so prevalent across the Arctic.

A Turbulence Scheme with Two Prognostic Turbulence Energies

A new turbulence scheme with two prognostic energies is presented. The scheme is an extension of a turbulence kinetic energy (TKE) scheme following the ideas of Zilitinkevich et al. but valid for the whole stability range and including the influence of moisture. The second turbulence prognostic energy is used only for a modification of the stability parameter. Thus, the scheme is downgradient, and the turbulent fluxes are proportional to the local gradients of the diffused variables. However, the stability parameter and consequently the turbulent exchange coefficients are not strictly local anymore and have a prognostic character. The authors believe that these characteristics enable the scheme to model both turbulence and clouds in the planetary boundary layer. The two-energy scheme was tested in three idealized single-column model (SCM) simulations, two in the convective boundary layer and one in the stable boundary layer. Overall, the scheme performs better than the standard TKE schemes. Compared to the TKE schemes, the two-energy scheme shows a more continuous behavior in time and space and mixes deeper in accordance with the LES results. A drawback of the scheme is that the modeled thermals tend to be too intense and too infrequent. This is due to the particular cutoff formulation of the chosen length-scale parameterization. Long-term three-dimensional global simulations show that the turbulence scheme behaves reasonable well in a full atmospheric model. In agreement with the SCM simulations, the scheme tends to overestimate cloud cover, especially at low levels.

Forcing Single-Column Models Using High-Resolution Model Simulations.

To use single‐column models (SCMs) as a research tool for parameterization development and process studies, the SCM must be supplied with realistic initial profiles, forcing fields, and boundary conditions. We propose a new technique for deriving these required profiles, motivated by the increase in number and scale of high‐resolution convection‐permitting simulations. We suggest that these high‐resolution simulations be coarse grained to the required resolution of an SCM, and thereby be used as a proxy for the true atmosphere. This paper describes the implementation of such a technique. We test the proposed methodology using high‐resolution data from the UK Met Office's Unified Model, with a resolution of 4 km, covering a large tropical domain. These data are coarse grained and used to drive the European Centre for Medium‐Range Weather Forecast's Integrated Forecasting System (IFS) SCM. The proposed method is evaluated by deriving IFS SCM forcing profiles from a consistent T639 IFS simulation. The SCM simulations track the global model, indicating a consistency between the estimated forcing fields and the true dynamical forcing in the global model. We demonstrate the benefits of selecting SCM forcing profiles from across a large domain, namely, robust statistics, and the ability to test the SCM over a range of boundary conditions. We also compare driving the SCM with the coarse‐grained data set to driving it using the European Centre for Medium‐Range Weather Forecast operational analysis. We conclude by highlighting the importance of understanding biases in the high‐resolution data set and suggest that our approach be used in combination with observationally derived forcing data sets.

Can We Use Single‐Column Models for Understanding the Boundary Layer Cloud‐Climate Feedback?

AbstractThis study explores how to drive Single‐Column Models (SCMs) with existing data sets of General Circulation Model (GCM) outputs, with the aim of studying the boundary layer cloud response to climate change in the marine subtropical trade wind regime. The EC‐EARTH SCM is driven with the large‐scale tendencies and boundary conditions as derived from two different data sets, consisting of high‐frequency outputs of GCM simulations. SCM simulations are performed near Barbados Cloud Observatory in the dry season (January–April), when fair‐weather cumulus is the dominant low‐cloud regime. This climate regime is characterized by a near equilibrium in the free troposphere between the long‐wave radiative cooling and the large‐scale advection of warm air. In the SCM, this equilibrium is ensured by scaling the monthly mean dynamical tendency of temperature and humidity such that it balances that of the model physics in the free troposphere. In this setup, the high‐frequency variability in the forcing is maintained, and the boundary layer physics acts freely. This technique yields representative cloud amount and structure in the SCM for the current climate. Furthermore, the cloud response to a sea surface warming of 4 K as produced by the SCM is consistent with that of the forcing GCM.

From Near-Neutral to Strongly Stratified: Adequately Modelling the Clear-Sky Nocturnal Boundary Layer at Cabauw

The performance of an atmospheric single-column model (SCM) is studied systematically for stably-stratified conditions. To this end, 11 years (2005–2015) of daily SCM simulations were compared to observations from the Cabauw observatory, The Netherlands. Each individual clear-sky night was classified in terms of the ambient geostrophic wind speed with a 1hbox { m} hbox { s}^{-1} bin-width. Nights with overcast conditions were filtered out by selecting only those nights with an average net radiation of less than -,30hbox { W }hbox {m}^{-2}. A similar procedure was applied to the observational dataset. A comparison of observed and modelled ensemble-averaged profiles of wind speed and potential temperature and time series of turbulent fluxes showed that the model represents the dynamics of the nocturnal boundary layer (NBL) at Cabauw very well for a broad range of mechanical forcing conditions. No obvious difference in model performance was found between near-neutral and strongly-stratified conditions. Furthermore, observed NBL regime transitions are represented in a natural way. The reference model version performs much better than a model version that applies excessive vertical mixing as is done in several (global) operational models. Model sensitivity runs showed that for weak-wind conditions the inversion strength depends much more on details of the land-atmosphere coupling than on the turbulent mixing. The presented results indicate that in principle the physical parametrizations of large-scale atmospheric models are sufficiently equipped for modelling stably-stratified conditions for a wide range of forcing conditions.

Stochasticity of convection in Giga-LES data

The poor representation of tropical convection in general circulation models (GCMs) is believed to be responsible for much of the uncertainty in the predictions of weather and climate in the tropics. The stochastic multicloud model (SMCM) was recently developed by Khouider et al. (Commun Math Sci 8(1):187–216, 2010) to represent the missing variability in GCMs due to unresolved features of organized tropical convection. The SMCM is based on three cloud types (congestus, deep and stratiform), and transitions between these cloud types are formalized in terms of probability rules that are functions of the large-scale environment convective state and a set of seven arbitrary cloud timescale parameters. Here, a statistical inference method based on the Bayesian paradigm is applied to estimate these key cloud timescales from the Giga-LES dataset, a 24-h large-eddy simulation (LES) of deep tropical convection (Khairoutdinov et al. in J Adv Model Earth Syst 1(12), 2009) over a domain comparable to a GCM gridbox. A sequential learning strategy is used where the Giga-LES domain is partitioned into a few subdomains, and atmospheric time series obtained on each subdomain are used to train the Bayesian procedure incrementally. Convergence of the marginal posterior densities for all seven parameters is demonstrated for two different grid partitions, and sensitivity tests to other model parameters are also presented. A single column model simulation using the SMCM parameterization with the Giga-LES inferred parameters reproduces many important statistical features of the Giga-LES run, without any further tuning. In particular it exhibits intermittent dynamical behavior in both the stochastic cloud fractions and the large scale dynamics, with periods of dry phases followed by a coherent sequence of congestus, deep, and stratiform convection, varying on timescales of a few hours consistent with the Giga-LES time series. The chaotic variations of the cloud area fractions were captured fairly well both qualitatively and quantitatively demonstrating the stochastic nature of convection in the Giga-LES simulation.

Attributing the behavior of low‐level clouds in large‐scale models to subgrid‐scale parameterizations

AbstractThis study explores ways of establishing the characteristic behavior of boundary layer schemes in representing subtropical marine low‐level clouds in climate models. To this purpose, parameterization schemes are studied in both isolated and interactive mode with the larger‐scale circulation. Results of the EUCLIPSE/GASS intercomparison study for Single‐Column Models (SCM) on low‐level cloud transitions are compared to General Circulation Model (GCM) results from the CFMIP‐2 project at selected grid points in the subtropical eastern Pacific. Low cloud characteristics are plotted as a function of key state variables for which Large‐Eddy Simulation results suggest a distinct and reasonably tight relation. These include the Cloud Top Entrainment Instability (CTEI) parameter and the total cloud cover. SCM and GCM results are thus compared and their resemblance is quantified using simple metrics. Good agreement is reported, to such a degree that SCM results are found to be uniquely representative of their GCM, and vice versa. This suggests that the system of parameterized fast boundary layer physics dominates the model state at any given time, even when interactive with the larger‐scale flow. This behavior can loosely be interpreted as a unique “fingerprint” of a boundary layer scheme, recognizable in both SCM and GCM simulations. The result justifies and advocates the use of SCM simulation for improving weather and climate models, including the attribution of typical responses of low clouds to climate change in a GCM to specific parameterizations.

Development of fine‐resolution analyses and expanded large‐scale forcing properties: 2. Scale awareness and application to single‐column model experiments

AbstractFine‐resolution three‐dimensional fields have been produced using the Community Gridpoint Statistical Interpolation (GSI) data assimilation system for the U.S. Department of Energy's Atmospheric Radiation Measurement Program (ARM) Southern Great Plains region. The GSI system is implemented in a multiscale data assimilation framework using the Weather Research and Forecasting model at a cloud‐resolving resolution of 2 km. From the fine‐resolution three‐dimensional fields, large‐scale forcing is derived explicitly at grid‐scale resolution; a subgrid‐scale dynamic component is derived separately, representing subgrid‐scale horizontal dynamic processes. Analyses show that the subgrid‐scale dynamic component is often a major component over the large‐scale forcing for grid scales larger than 200 km. The single‐column model (SCM) of the Community Atmospheric Model version 5 is used to examine the impact of the grid‐scale and subgrid‐scale dynamic components on simulated precipitation and cloud fields associated with a mesoscale convective system. It is found that grid‐scale size impacts simulated precipitation, resulting in an overestimation for grid scales of about 200 km but an underestimation for smaller grids. The subgrid‐scale dynamic component has an appreciable impact on the simulations, suggesting that grid‐scale and subgrid‐scale dynamic components should be considered in the interpretation of SCM simulations.

Diurnal course analysis of the WRF-simulated and observation-based planetary boundary layer height

Abstract. Weather Research and Forecasting (WRF) single-column model simulations were performed in the late summer of 2012 in order to analyse the diurnal changes of the planetary boundary layer (PBL). Five PBL schemes were tested with the WRF. From the radiometer and wind-profiler measurements at one station, derived PBL heights were also compared to the simulations. The weather conditions during the measurement period proved to be dry; the soil moisture was below wilting point 85 percent of the time. Results show that (1) simulation-based PBL heights are overestimated by about 500–1000 m with respect to the observation-based PBL heights, and (2) PBL height deviations between different observation-based methods (around 700 m in the midday) are comparable with PBL height deviations between different model schemes used in the WRF single-column model. The causes of the deviations are also discussed. It is shown that in the estimation of the PBL height the relevance of the atmospheric profiles could be as important as the relevance of the estimation principles.

A single‐column model ensemble approach applied to the TWP‐ICE experiment

Single‐column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large‐scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large‐scale data for the Tropical Warm Pool International Cloud Experiment (TWP‐ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud‐resolving models (CRM). Best estimate simulations are also performed. All models show that moisture‐related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

Derivation of Turbulent Kinetic Energy from a First-Order Nonlocal Planetary Boundary Layer Parameterization

Abstract Turbulent kinetic energy (TKE) is derived from a first-order planetary boundary layer (PBL) parameterization for convective boundary layers: the nonlocal K-profile Yonsei University (YSU) PBL. A parameterization for the TKE equation is developed to calculate TKE based on meteorological profiles given by the YSU PBL model. For this purpose buoyancy- and shear-generation terms are formulated consistently with the YSU scheme—that is, the combination of local, nonlocal, and explicit entrainment fluxes. The vertical transport term is also formulated in a similar fashion. A length scale consistent with the K profile is suggested for parameterization of dissipation. Single-column model (SCM) simulations are conducted for a period in the second Global Energy and Water Cycle Experiment (GEWEX) Atmospheric Boundary Layer Study (GABLS2) intercomparison case. Results from the SCM simulations are compared with large-eddy simulation (LES) results. The daytime evolution of the vertical structure of TKE matches well with mixed-layer development. The TKE profile is shaped like a typical vertical velocity (w) variance, and its maximum is comparable to that from the LES. By varying the dissipation length from −23% to +13% the TKE maximum is changed from about −15% to +7%. After normalization, the change does not exceed the variability among previous studies. The location of TKE maximum is too low without the effects of the nonlocal TKE transport.