Continuous Single-Column Model Evaluation at a Permanent Meteorological Supersite

Uncertainties in numerical predictions of weather and climate are often linked to the representation of unresolved processes that act relatively quickly compared to the resolved general circulation. These processes include turbulence, convection, clouds, and radiation. Single-column model (SCM) simulation of idealized cases and the subsequent evaluation against large-eddy simulation (LES) results has become an often used and relied on method to obtain insight at process level into the behavior of such parameterization schemes; benefits of SCM simulation are the enhanced model transparency and the high computational efficiency. Although this approach has achieved demonstrable success, some shortcomings have been identified; among these, i) the statistical significance and relevance of single idealized case studies might be questioned and ii) the use of observational datasets has been relatively limited. A recently initiated project named the Royal Netherlands Meteorological Institute (KNMI) Parameterization Testbed (KPT) is part of a general move toward a more statistically significant process-level evaluation, with the purpose of optimizing the identification of problems in general circulation models that are related to parameterization schemes. The main strategy of KPT is to apply continuous long-term SCM simulation and LES at various permanent meteorological sites, in combination with comprehensive evaluation against observations at multiple time scales. We argue that this strategy enables the reproduction of typical long-term mean behavior of fast physics in large-scale models, but it still preserves the benefits of single-case studies (such as model transparency). This facilitates the tracing and understanding of errors in parameterization schemes, which should eventually lead to a reduction of related uncertainties in numerical predictions of weather and climate

Overlap Statistics of Cumuliform Boundary-Layer Cloud Fields in Large-Eddy Simulations

Overlap statistics of cumuliform boundary-layer clouds are studied using large-eddy simulations at high resolutions. The cloud overlap is found to be highly inefficient, due to the typical irregularity of cumuliform clouds over a wide range of scales. The detection of such inefficient overlap is enabled in this study by i) applying fine enough discretizations and ii) by limiting the analysis to exclusively cumuliform boundary-layer cloud fields. It is argued that these two factors explain the differences with some previous studies on cloud overlap. In contrast, good agreement exists with previously reported observations of cloud overlap as derived from lidar measurements of liquid water clouds at small cloud covers. Various candidate functional forms are fitted to the results, suggesting that an inverse linear function is most successful in reproducing the observed behavior. The sensitivity of cloud overlap to various aspects is assessed, reporting a minimal or non-systematic dependence on discretization and vertical wind-shear, as opposed to a strong case-dependence, the latter probably reflecting differences in the cloud size distribution. Finally, calculations with an offline radiation scheme suggest that accounting for the inefficient overlap in cumuliform cloud fields in a general circulation model can change the top-of-atmosphere short-wave cloud radiative forcing by −20 to −40 W m−2, depending on vertical discretization. This corresponds to about 50 to 100% of the typical values in areas of persistent shallow cumulus, respectively

Overlap Statistics of Cumuliform Boundary-Layer Cloud Fields in Large-Eddy Simulations

Overlap statistics of cumuliform boundary-layer clouds are studied using large-eddy simulations at high resolutions. The cloud overlap is found to be highly inefficient, due to the typical irregularity of cumuliform clouds over a wide range of scales. The detection of such inefficient overlap is enabled in this study by i) applying fine enough discretizations and ii) by limiting the analysis to exclusively cumuliform boundary-layer cloud fields. It is argued that these two factors explain the differences with some previous studies on cloud overlap. In contrast, good agreement exists with previously reported observations of cloud overlap as derived from lidar measurements of liquid water clouds at small cloud covers. Various candidate functional forms are fitted to the results, suggesting that an inverse linear function is most successful in reproducing the observed behavior. The sensitivity of cloud overlap to various aspects is assessed, reporting a minimal or non-systematic dependence on discretization and vertical wind-shear, as opposed to a strong case-dependence, the latter probably reflecting differences in the cloud size distribution. Finally, calculations with an offline radiation scheme suggest that accounting for the inefficient overlap in cumuliform cloud fields in a general circulation model can change the top-of-atmosphere short-wave cloud radiative forcing by −20 to −40 W m−2, depending on vertical discretization. This corresponds to about 50 to 100% of the typical values in areas of persistent shallow cumulus, respectively

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.</p

Stochastic parameterization of shallow cumulus convection estimated from high-resolution data

In this paper, we report on the development of a methodology for stochastic parameterization of convective transport by shallow cumulus convection in weather and climate models. We construct a parameterization based on Large-Eddy Simulation (LES) data. These simulations resolve the turbulent fluxes of heat and moisture and are based on a typical case of non-precipitating shallow cumulus convection above sea in the trade-wind region. Using clustering, we determine a finite number of turbulent flux pairs for heat and moisture that are representative for the pairs of flux profiles observed in these simulations. In the stochastic parameterization scheme proposed here, the convection scheme jumps randomly between these pre-computed pairs of turbulent flux profiles. The transition probabilities are estimated from the LES data, and they are conditioned on the resolved-scale state in the model column. Hence, the stochastic parameterization is formulated as a data-inferred conditional Markov chain (CMC), where each state of the Markov chain corresponds to a pair of turbulent heat and moisture fluxes. The CMC parameterization is designed to emulate, in a statistical sense, the convective behaviour observed in the LES data. The CMC is tested in single-column model (SCM) experiments. The SCM is able to reproduce the ensemble spread of the temperature and humidity that was observed in the LES data. Furthermore, there is a good similarity between time series of the fractions of the discretized fluxes produced by SCM and observed in LES

Shallow Cumulus Cloud Fields Are Optically Thicker When They Are More Clustered

Shallow trade cumuli over subtropical oceans are a persistent source of uncertainty in climate projections. Mesoscale organization of trade cumulus clouds has been shown to influence their cloud radiative effect (CRE) through cloud cover. We investigate whether organization can explain CRE variability independently of cloud cover variability. By analyzing satellite observations and high-resolution simulations, we show that increased clustering leads to geometrically thicker clouds with larger domain-averaged liquid water paths, smaller cloud droplets, and consequently, larger cloud optical depths. The relationships between these variables are shaped by the mixture of deep cloud cores and shallower interstitial clouds or anvils that characterize cloud organization. Eliminating cloud cover effects, more clustered clouds reflect up to 20 W/m$^2$ more instantaneous shortwave radiation back to space

Stochastic Parameterization of Convective Area Fractions with a Multicloud Model Inferred from Observational Data

Observational data of rainfall from a rain radar in Darwin, Australia, are combined with data defining the large-scale dynamic and thermodynamic state of the atmosphere around Darwin to develop a multicloud model based on a stochastic method using conditional Markov chains. The authors assign the radar data to clear sky, moderate congestus, strong congestus, deep convective, or stratiform clouds and estimate transition probabilities used by Markov chains that switch between the cloud types and yield cloud-type area fractions. Cross-correlation analysis shows that the mean vertical velocity is an important indicator of deep convection. Further, it is shown that, if conditioned on the mean vertical velocity, the Markov chains produce fractions comparable to the observations. The stochastic nature of the approach turns out to be essential for the correct production of area fractions. The stochastic multicloud model can easily be coupled to existing moist convectio