ALBERT

Description: The role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. The parameterization of cloud microphysical processes in weather and climate models is a well‐known source of uncertainties. Hence, robust quantification of this uncertainty is mandatory. Sensitivity analysis to date has typically investigated only a few model parameters. We propose algorithmic differentiation (AD) as a tool to detect the magnitude and timing at which a model state variable is sensitive to any of the hundreds of uncertain model parameters in the cloud microphysics parameterization. AD increases the computational cost by roughly a third in our simulations. We explore this methodology as the example of warm conveyor belt trajectories, that is, air parcels rising rapidly from the planetary boundary layer to the upper troposphere in the vicinity of an extratropical cyclone. Based on the information of derivatives with respect to the uncertain parameters, the ten parameters contributing most to uncertainty are selected. These uncertain parameters are mostly related to the representation of hydrometeor diameter and fall velocity, the activation of cloud condensation nuclei, and heterogeneous freezing. We demonstrate the meaningfulness of the AD‐estimated sensitivities by comparing the AD results with ensemble simulations spawned at different points along the trajectories, where different parameter settings are used in the various ensemble members. The ranking of the most important parameters from these ensemble simulations is consistent with the results from AD. Thus, AD is a helpful tool for selecting parameters contributing most to cloud microphysics uncertainty.

Description: Plain Language Summary: The formation of clouds is determined by processes that act on smaller scales than weather prediction models can resolve. Consequently, a parameterization with typically hundreds of parameters is constructed to determine the effects of these processes on the resolved larger scales. These parameters are a well‐known source of uncertainty in weather and climate models. Classical attempts to quantify this uncertainty are typically limited to a few parameters. We propose algorithmic differentiation (AD) as a tool to detect parameters with the largest impact for any of the hundreds of parameters on multiple model state variables at every time step in our simulation. This increases the computational cost by roughly a third. The relevance of the AD‐estimated impact is demonstrated by comparing the AD results with ensemble simulations, where different parameter settings are used in the various ensemble members. The ranking of the most important parameters from these ensemble simulations is consistent with the results from AD. Thus, AD is a helpful tool to identify parameters objectively that contribute most to uncertainty in cloud parameterizations.

Description: Key Points: Quantification of multi‐parameter uncertainty of cloud microphysical evolution of WCB trajectories using algorithmic differentiation. Uncertainty at every time step derived with algorithmic differentiation representative for key uncertainty over at least 30 min intervals. Parameterization of CCN activation, diameter size, and fall velocity of hydrometeors have the largest mean impact on water vapor contents.

Description: Deutsch Forschungsgemeinschaft DFG

Description: Desert dust is one of the most important atmospheric ice-nucleating aerosol species around the globe. However, there have been very few measurements of ice-nucleating particle (INP) concentrations in dusty air close to desert sources. In this study we report the concentration of INPs in dust laden air over the tropical Atlantic within a few days' transport of one of the world's most important atmospheric sources of desert dust, the Sahara. These measurements were performed as part of the Ice in Clouds Experiment-Dust campaign based in Cape Verde, during August 2015. INP concentrations active in the immersion mode, determined using a droplet-on-filter technique, ranged from around 102 m−3 at −12°C to around 105 m−3 at −23°C. There is about 2 orders of magnitude variability in INP concentration for a particular temperature, which is determined largely by the variability in atmospheric dust loading. These measurements were made at altitudes from 30 to 3,500 m in air containing a range of dust loadings. The ice active site density (ns) for desert dust dominated aerosol derived from our measurements agrees with several laboratory-based parameterizations for ice nucleation by desert dust within 1 to 2 orders of magnitude. The small variability in ns values determined from our measurements (within about 1 order of magnitude) is striking given that the back trajectory analysis suggests that the sources of dust were geographically diverse. This is consistent with previous work, which indicates that desert dust's ice-nucleating activity is only weakly dependent on source. ©2018. The Authors.

Description: A module to calculate online trajectories has been implemented into the nonhydrostatic limited-area weather prediction and climate model COSMO. Whereas offline trajectories are calculated with wind fields from model output, which is typically available every one to six hours, online trajectories use the simulated resolved wind field at every model time step (typically less than a minute) to solve the trajectory equation. As a consequence, online trajectories much better capture the short-term temporal fluctuations of the wind field, which is particularly important for mesoscale flows near topography and convective clouds, and they do not suffer from temporal interpolation errors between model output times. The numerical implementation of online trajectories in the COSMO-model is based upon an established offline trajectory tool and takes full account of the horizontal domain decomposition that is used for parallelization of the COSMO-model. Although a perfect workload balance cannot be achieved for the trajectory module (due to the fact that trajectory positions are not necessarily equally distributed over the model domain), the additional computational costs are found to be fairly small for the high-resolution simulations described in this paper. The computational costs may, however, vary strongly depending on the number of trajectories and trace variables. Various options have been implemented to initialize online trajectories at different locations and times during the model simulation. As a first application of the new COSMO-model module, an Alpine north foehn event in summer 1987 has been simulated with horizontal resolutions of 2.2, 7 and 14 km. It is shown that low-tropospheric trajectories calculated offline with one- to six-hourly wind fields can significantly deviate from trajectories calculated online. Deviations increase with decreasing model grid spacing and are particularly large in regions of deep convection and strong orographic flow distortion. On average, for this particular case study, horizontal and vertical positions between online and offline trajectories differed by 50–190 km and 150–750 m, respectively, after 24 h. This first application illustrates the potential for Lagrangian studies of mesoscale flows in high-resolution convection-resolving simulations using online trajectories.

Description: Simulations of cirrus are subject to uncertainties in model physics and meteorological input data. Here we model cirrus clouds, whose extinction has been measured with an elastic backscatter Lidar at Jungfraujoch research station in the Swiss Alps, and investigate the sensitivities to input data uncertainties (trajectory resolution, unresolved vertical velocities, ice nuclei number density and upstream specific humidity). Simulations with a microphysical stacked box model have been performed along trajectories derived from the high-resolution numerical weather prediction model COSMO-2 (2.2 km grid spacing). For the calculation of the trajectories we experimented with model wind fields at temporal resolutions between 20 s and 1 h. While the temporal resolution affects the trajectory path only marginally, it has a strong impact on the vertical velocity variance resolved along the trajectories, and therefore on the cooling rate distribution. In the present example, the temporal resolution of the wind fields must be chosen to be better than 5 min in order to resolve vertical velocities and cooling rates required to explain the measured extinction. The simulation improves slightly if the temporal resolution is increased further to 20 s. This means that on the selected day the cooling rate spectra calculated by COSMO-2 suffice to achieve agreement with the cirrus measurements. On that day cooling rate spectra are characterized bysignificantly lower vertical velocity amplitudes than those found previously in some aircraft campaigns (SUCCESS, MACPEX). A climatological analysis of the vertical velocity variance in the Alpine region based on COSMO-2 analyses and balloon sounding data suggests large day-to-day variability in small-scale temperature fluctuations. This demonstrates the necessity to apply numerical weather prediction models with high spatial and temporal resolutions in cirrus modeling, whereas using climatological means for the amplitude of the unresolved air motions does generally not suffice. The box model simulations further suggest that uncertainties in the upstream specific humidity (±10% of the model prediction) and in the ice nuclei number density are more important for the modeled cirrus cloud than the unresolved temperature fluctuations, if temporally highly resolved trajectories are used. For the presented case the simulations are incompatible with ice nuclei number densities larger than 20 L−1 and insensitive to variations below this value.

Description: A module to calculate online trajectories has been implemented into the non-hydrostatic limited-area weather prediction and climate model COSMO. Whereas offline trajectories are calculated with wind fields from model output, which is typically available every one to six hours, online trajectories use the simulated wind field at every model time step (typically less than a minute) to solve the trajectory equation. As a consequence, online trajectories much better capture the short-term temporal fluctuations of the wind field, which is particularly important for mesoscale flows near topography and convective clouds, and they do not suffer from temporal interpolation errors between model output times. The numerical implementation of online trajectories in the COSMO model is based upon an established offline trajectory tool and takes full account of the horizontal domain decomposition that is used for parallelization of the COSMO model. Although a perfect workload balance cannot be achieved for the trajectory module (due to the fact that trajectory positions are not necessarily equally distributed over the model domain), the additional computational costs are fairly small for high-resolution simulations. Various options have been implemented to initialize online trajectories at different locations and times during the model simulation. As a first application of the new COSMO module an Alpine North Föhn event in summer 1987 has been simulated with horizontal resolutions of 2.2 km, 7 km, and 14 km. It is shown that low-tropospheric trajectories calculated offline with one- to six-hourly wind fields can significantly deviate from trajectories calculated online. Deviations increase with decreasing model grid spacing and are particularly large in regions of deep convection and strong orographic flow distortion. On average, for this particular case study, horizontal and vertical positions between online and offline trajectories differed by 50–190 km and 150–750 m, respectively, after 24 h. This first application illustrates the potential for Lagrangian studies of mesoscale flows in high-resolution convection-resolving simulations using online trajectories.

Description: Simulations of cirrus are subject to uncertainties in model physics and meteorological input data. Here we model cirrus clouds along air mass trajectories, whose extinction has been measured with an elastic backscatter lidar at Jungfraujoch research station in the Swiss Alps, with a microphysical stacked box model. The sensitivities of these simulations to input data uncertainties (trajectory resolution, unresolved vertical velocities, ice nuclei number density and upstream specific humidity) are investigated. Variations in the temporal resolution of the wind field data (COSMO-Model at 2.2 km resolution) between 20 s and 1 h have only a marginal impact on the trajectory path, while the representation of the vertical velocity variability and therefore the cooling rate distribution are significantly affected. A temporal resolution better than 5 min must be chosen in order to resolve cooling rates required to explain the measured extinction. A further increase in the temporal resolution improves the simulation results slightly. The close match between the modelled and observed extinction profile for high-resolution trajectories suggests that the cooling rate spectra calculated by the COSMO-2 model suffice on the selected day. The modelled cooling rate spectra are, however, characterized by significantly lower vertical velocity amplitudes than those found previously in some aircraft campaigns (SUCCESS, MACPEX). A climatological analysis of the vertical velocity amplitude in the Alpine region based on COSMO-2 analyses and balloon sounding data suggests large day-to-day variability in small-scale temperature fluctuations. This demonstrates the necessity to apply numerical weather prediction models with high spatial and temporal resolutions in cirrus modelling, whereas using climatological means for the amplitude of the unresolved air motions does generally not suffice. The box model simulations further suggest that uncertainties in the upstream specific humidity (± 10 % of the model prediction) and in the ice nuclei number density (0–100 L−1) are more important for the modelled cirrus cloud than the unresolved temperature fluctuations if temporally highly resolved trajectories are used. For the presented case the simulations are incompatible with ice nuclei number densities larger than 20 L−1 and insensitive to variations below this value.

Description: The characteristic and strongly precipitating cloud band in extratropical cyclones is associated with the so-called warm conveyor belt (WCB), which is a coherent airstream ascending cross-isentropically from the boundary layer into the upper troposphere within two days. The WCB ascent behaviour and associated diabatic heating are influenced by microphysical processes and environmental conditions in the WCB inflow region. The former relies on parametrisation schemes, the latter on initial and/or boundary conditions of thermodynamic variables. Altogether, this introduces uncertainty in numerical weather prediction and ultimately for the evolution of the large-scale mid-latitude flow. Based on a case study from the NAWDEX field campaign, we quantify the relative importance of perturbations to various microphysical processes and WCB inflow temperature and moisture (via modification of sea surface temperature). Thereby, we focus on uncertainty in WCB ascent behaviour, associated precipitation characteristics, as well as properties of the amplifying ridge downstream of the ascent region. To disentangle individual uncertainty contributions, we build a 70-member perturbed parameter ensemble which systematically combines the perturbations, and subsequently perform variance-based sensitivity analysis. Our results suggests that changes to WCB inflow properties most strongly influence WCB ascent behavior, surface precipitation sums, and the ridge amplitude. Yet, the microphysical perturbations locally modify vertical velocity in the WCB and determine the precipitation efficiency, which affects local to meso-scale precipitation characteristics and its spatial distribution. Moreover, the microphysical perturbations are distinctly imprinted in the large-scale flow pattern - albeit to a lesser extent than the perturbations applied to the WCB inflow characteristics.

Description: Hail formation in deep convective storms depends strongly on environmental meteorological and aerosol conditions. Here we investigate the impact of uncertainty in ambient conditions and cloud microphysics representation for simulated (hail) precipitation over Southwestern Germany on 28th July 2013, the day of the Andreas hailstorm. We perform model simulations on convection-resolving scale with the numerical weather prediction model ICON coupled with the aerosol module ART. We generated a perturbed parameter ensemble with 90 ensemble members to sample uncertainties in cloud-, precipitation- and hail-related variables. Six parameters were jointly perturbed: cloud condensation nuclei (CCN) and ice nucleation particle (INP) concentrations, riming efficiency of graupel and hail, atmospheric stability, and vertical wind shear. Analysis of the ensemble with statistical emulation and variance analysis shows the importance of the CCN concentration and stability for controlling the amount of surface hail and total precipitation in the model. The geographical distribution of hail and precipitation shows a large variety among the ensemble members, with storm tracks shifted further to the north or south compared to the reference simulation. The path of the storm track is thereby mainly controlled by stability and vertical wind shear, however, aerosol parameters seem to be important for the number of storm cells.

Description: Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays an important role but so do potentially uncertainties arising from the representation of subgrid-scale processes, e.g. cloud microphysics. In this project, we investigate the impact of cloud microphysical parameter uncertainties on the forecast of a selected severe convective storm over South-Eastern Germany in 2019. A perturbed parameter ensemble with 112 members has been generated with the ICON model (2-moment cloud microphysics, 1 km grid-spacing). Uncertain parameters in the representation of (i) CCN and INP activation, (ii) diffusional growth of ice and snow, (iii) riming of graupel and hail, as well as (iv) the mass-diameter and mass-fall velocity relations for graupel and hail are considered. To generate systematic parameter variations from the eight-dimensional parameter space a latin hypercube sampling is used. The storm properties, including cloud microphysical structure and surface (hail) precipitation, are determined by identifying storm objects with a storm tracking and watershedding algorithm. Results indicate a large impact of INP concentration on precipitation and surface hail amounts. Furthermore, a strong interaction of INP and CCN concentrations is found with increased sensitivity to INP concentrations at low CCN concentrations. Statistical emulation and variance-based sensitivity analysis further indicate substantial impact of graupel density and ice capacitance on surface precipitation and hail content respectively. Additionally, an initial condition ensemble is simulated. First insights into the relative importance of initial condition uncertainty and microphysical parameter uncertainty will be presented.