ALBERT

Description: Large biases in climate model simulations of cloud radiative properties over the Southern Ocean cause large errors in modeled sea surface temperatures, atmospheric circulation, and climate sensitivity. Here, we combine cloud-resolving model simulations with estimates of the concentration of ice-nucleating particles in this region to show that our simulated Southern Ocean clouds reflect far more radiation than predicted by global models, in agreement with satellite observations. Specifically, we show that the clouds that are most sensitive to the concentration of ice-nucleating particles are low-level mixed-phase clouds in the cold sectors of extratropical cyclones, which have previously been identified as a main contributor to the Southern Ocean radiation bias. The very low ice-nucleating particle concentrations that prevail over the Southern Ocean strongly suppress cloud droplet freezing, reduce precipitation, and enhance cloud reflectivity. The results help explain why a strong radiation bias occurs mainly in this remote region away from major sources of ice-nucleating particles. The results present a substantial challenge to climate models to be able to simulate realistic ice-nucleating particle concentrations and their effects under specific meteorological conditions.

Description: This manuscript introduces a new technique for evaluating lightning forecasts from convection-permitting models. In recent years, numerical weather prediction models at the convection-permitting scales (horizontal grid resolutions of 1–5 km) have been able to produce realistic-looking forecasts of lightning activity when compared with observations. However, it is challenging to assess what value these forecasts add above standard large-scale indices. Examining this problem, it is found that existing skill scores and neighborhood verification methods are unable to cope with both the double-penalty effect and the model’s variable frequency bias. A displacement distance and a quasi-symmetric distance score are introduced based on the distance between the model and the observations, the latter showing any improvement the forecast has over a completely “hedged” forecast. This can be combined with a domain-improved contingency table and comparisons between modeled and observed lightning flashes to evaluate the forecast performance in three important dimensions: coverage, distance, and intensity. The verification metric is illustrated with a single case, which shows that the convective-scale U.K. variable resolution model (UKV) delivers improved forecasts compared with the large-scale indices in both coverage and distance. Additionally, a month-long analysis is performed, which reveals that the coverage of lightning is in good agreement with the observations; lightning is displaced by the model by a distance on the order of 50–75 km, but the model overpredicts the lightning intensity by at least a factor of 6 after observational detection efficiencies have been considered.

Description: Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

Description: A convection-permitting multiyear regional climate simulation using the Met Office Unified Model has been run for the first time on an Africa-wide domain. The model has been run as part of the Future Climate for Africa (FCFA) Improving Model Processes for African Climate (IMPALA) project, and its configuration, domain, and forcing data are described here in detail. The model [Pan-African Convection-Permitting Regional Climate Simulation with the Met Office UM (CP4-Africa)] uses a 4.5-km horizontal grid spacing at the equator and is run without a convection parameterization, nested within a global atmospheric model driven by observations at the sea surface, which does include a convection scheme. An additional regional simulation, with identical resolution and physical parameterizations to the global model, but with the domain, land surface, and aerosol climatologies of CP4-Africa, has been run to aid in the understanding of the differences between the CP4-Africa and global model, in particular to isolate the impact of the convection parameterization and resolution. The effect of enforcing moisture conservation in CP4-Africa is described and its impact on reducing extreme precipitation values is assessed. Preliminary results from the first five years of the CP4-Africa simulation show substantial improvements in JJA average rainfall compared to the parameterized convection models, with most notably a reduction in the persistent dry bias in West Africa, giving an indication of the benefits to be gained from running a convection-permitting simulation over the whole African continent.

Description: In recent years, a growing partnership has emerged between the Met Office and the designated U.S. national centers for expertise in severe weather research and forecasting, that is, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) and the NOAA Storm Prediction Center (SPC). The driving force behind this partnership is a compelling set of mutual interests related to predicting and understanding high-impact weather and using high-resolution numerical weather prediction models as foundational tools to explore these interests. The forum for this collaborative activity is the NOAA Hazardous Weather Testbed, where annual Spring Forecasting Experiments (SFEs) are conducted by NSSL and SPC. For the last decade, NSSL and SPC have used these experiments to find ways that high-resolution models can help achieve greater success in the prediction of tornadoes, large hail, and damaging winds. Beginning in 2012, the Met Office became a contributing partner in annual SFEs, bringing complementary expertise in the use of convection-allowing models, derived in their case from a parallel decadelong effort to use these models to advance prediction of flash floods associated with heavy thunderstorms. The collaboration between NSSL, SPC, and the Met Office has been enthusiastic and productive, driven by strong mutual interests at a grassroots level and generous institutional support from the parent government agencies. In this article, a historical background is provided, motivations for collaborative activities are emphasized, and preliminary results are highlighted.

Description: Hail and graupel are linked to lightning production and are important components of cloud evolution. Hail can also cause significant damage when it precipitates to the surface. The accurate prediction of the amount and location of hail and graupel and the effects on the other hydrometeor species depends upon the size distribution assumed. Here, we use ~310 km of in situ observations from flights of the South Dakota School of Mines and Technology T-28 storm-penetrating aircraft to constrain the representation of the particle size distribution (PSD) of hail. The maximum ~1-km hail water content encountered was 9 g m−3. Optical probe PSD measurements are normalized using two-moment normalization relations to obtain an underlying exponential shape. By linking the two normalizing moments through a power law, a parameterization of the hail PSD is provided based on the hail water content only. Preliminary numerical weather simulations indicate that the new parameterization produces increased radar reflectivity relative to commonly used PSD representations.

Description: Changes induced by perturbed aerosol conditions in moderately deep mixed-phase convective clouds (cloud top height ∼ 5 km) developing along sea-breeze convergence lines are investigated with high-resolution numerical model simulations. The simulations utilise the newly developed Cloud–AeroSol Interacting Microphysics (CASIM) module for the Unified Model (UM), which allows for the representation of the two-way interaction between cloud and aerosol fields. Simulations are evaluated against observations collected during the COnvective Precipitation Experiment (COPE) field campaign over the southwestern peninsula of the UK in 2013. The simulations compare favourably with observed thermodynamic profiles, cloud base cloud droplet number concentrations (CDNC), cloud depth, and radar reflectivity statistics. Including the modification of aerosol fields by cloud microphysical processes improves the correspondence with observed CDNC values and spatial variability, but reduces the agreement with observations for average cloud size and cloud top height. Accumulated precipitation is suppressed for higher-aerosol conditions before clouds become organised along the sea-breeze convergence lines. Changes in precipitation are smaller in simulations with aerosol processing. The precipitation suppression is due to less efficient precipitation production by warm-phase microphysics, consistent with parcel model predictions. In contrast, after convective cells organise along the sea-breeze convergence zone, accumulated precipitation increases with aerosol concentrations. Condensate production increases with the aerosol concentrations due to higher vertical velocities in the convective cores and higher cloud top heights. However, for the highest-aerosol scenarios, no further increase in the condensate production occurs, as clouds grow into an upper-level stable layer. In these cases, the reduced precipitation efficiency (PE) dominates the precipitation response and no further precipitation enhancement occurs. Previous studies of deep convective clouds have related larger vertical velocities under high-aerosol conditions to enhanced latent heating from freezing. In the presented simulations changes in latent heating above the 0∘C are negligible, but latent heating from condensation increases with aerosol concentrations. It is hypothesised that this increase is related to changes in the cloud field structure reducing the mixing of environmental air into the convective core. The precipitation response of the deeper mixed-phase clouds along well-established convergence lines can be the opposite of predictions from parcel models. This occurs when clouds interact with a pre-existing thermodynamic environment and cloud field structural changes occur that are not captured by simple parcel model approaches.

Description: Aerosol–cloud interactions are a major source of uncertainty in inferring the climate sensitivity from the observational record of temperature. The adjustment of clouds to aerosol is a poorly constrained aspect of these aerosol–cloud interactions. Here, we examine the response of midlatitude cyclone cloud properties to a change in cloud droplet number concentration (CDNC). Idealized experiments in high-resolution, convection-permitting global aquaplanet simulations with constant CDNC are compared to 13 years of remote-sensing observations. Observations and idealized aquaplanet simulations agree that increased warm conveyor belt (WCB) moisture flux into cyclones is consistent with higher cyclone liquid water path (CLWP). When CDNC is increased a larger LWP is needed to give the same rain rate. The LWP adjusts to allow the rain rate to be equal to the moisture flux into the cyclone along the WCB. This results in an increased CLWP for higher CDNC at a fixed WCB moisture flux in both observations and simulations. If observed cyclones in the top and bottom tercile of CDNC are contrasted it is found that they have not only higher CLWP but also cloud cover and albedo. The difference in cyclone albedo between the cyclones in the top and bottom third of CDNC is observed by CERES to be between 0.018 and 0.032, which is consistent with a 4.6–8.3 Wm−2 in-cyclone enhancement in upwelling shortwave when scaled by annual-mean insolation. Based on a regression model to observed cyclone properties, roughly 60 % of the observed variability in CLWP can be explained by CDNC and WCB moisture flux.

Description: The relative contribution of variations in meteorological and aerosol initial and boundary conditions to the variability in modelled cloud properties is investigated with a high-resolution ensemble (30 members). In the investigated case, moderately deep convection develops along sea-breeze convergence zones over the southwestern peninsula of the UK. A detailed analysis of the mechanism of aerosol–cloud interactions in this case has been presented in the first part of this study (Miltenberger et al., 2018). The meteorological ensemble (10 members) varies by about a factor of 2 in boundary-layer moisture convergence, surface precipitation, and cloud fraction, while aerosol number concentrations are varied by a factor of 100 between the three considered aerosol scenarios. If ensemble members are paired according to the meteorological initial and boundary conditions, aerosol-induced changes are consistent across the ensemble. Aerosol-induced changes in CDNC (cloud droplet number concentration), cloud fraction, cell number and size, outgoing shortwave radiation (OSR), instantaneous and mean precipitation rates, and precipitation efficiency (PE) are statistically significant at the 5 % level, but changes in cloud top height or condensate gain are not. In contrast, if ensemble members are not paired according to meteorological conditions, aerosol-induced changes are statistically significant only for CDNC, cell number and size, outgoing shortwave radiation, and precipitation efficiency. The significance of aerosol-induced changes depends on the aerosol scenarios compared, i.e. an increase or decrease relative to the standard scenario. A simple statistical analysis of the results suggests that a large number of realisations (typically 〉100) of meteorological conditions within the uncertainty of a single day are required for retrieving robust aerosol signals in most cloud properties. Only for CDNC and shortwave radiation small samples are sufficient. While the results are strictly only valid for the investigated case, the presented evidence combined with previous studies highlights the necessity for careful consideration of intrinsic predictability, meteorological conditions, and co-variability between aerosol and meteorological conditions in observational or modelling studies on aerosol indirect effects.