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WG3 discussed both the pros and cons of existing schemes as Working group 1 considered the treatment of model uncertainty (MU) in high-resolution ensembles, at grid spacings of order 1-5 km. These systems are often run for regional weather forecasting, perhaps over a single country, and for lead times of up to 5 days. Looking ahead, ECMWF’s strategy seeks to deliver global medium-range ensemble forecasts with 3-4 km grid spacings by 2030. It is questionable for what grid spacing we should dispense with a deep convection parameterization, but it will be either switched off or damped in these systems, such that deep convection can be assumed to be dominated by explicit motions. One of the problems with limited-area ensemble systems at this scale is that spread depends not only on the modelling system itself but also on the variability inherited from the large-scale boundary conditions. There is often thought to be a lack of spread in our high-resolution EPS (ensemble prediction systems), but this could reflect a lack of diversity on larger scales. The relative importance of lateral-boundary diversity and the model uncertainty mechanisms is regime dependent. The lateral boundaries will generally be more important in midlatitude winter but less so for summertime convection in relatively weak synoptic flow.

Current methods to evaluate gene-by-environment (GxE) interactions on biobank-scale datasets are limited. MonsterLM enables multiple linear regression on genome-wide datasets, does not rely on parameters specification and provides unbiased estimates of variance explained by GxE interaction effects. We applied MonsterLM to the UK Biobank for eight blood biomarkers (N=325,991), identifying significant genome-wide interaction variance with waist-to-hip ratio for five biomarkers, with variance explained by interactions ranging from 0.11 to 0.58. 48% to 94% of GxE interaction variance can be attributed to variants without significant marginal association with the phenotype of interest. Conversely, for most traits, >40% of interaction variance was explained by less than 5% of genetic variants. We observed significant improvements in polygenic score prediction with incorporation of GxE interactions in four biomarkers. Our results imply an important contribution of GxE interaction effects, driven largely by a restricted set of variants distinct from loci with strong marginal effects.

In common with many global models, the Met Office Unified Model (MetUM) climate simulations show large errors in Indian summer monsoon rainfall, with a wet bias over the equatorial Indian Ocean, a dry bias over India, and with too weak low-level flow into India. The representation of moist convection is a dominant source of error in global models, where convection must be parametrised, with the errors growing quickly enough to affect both weather and climate simulations. Here we use the first multi- week continental-scale MetUM simulations over India, with grid-spacings that allow explicit convection, to examine how convective parametrisation contributes to model biases in the region. Some biases are improved in the convection-permitting simulations with more intense rainfall over India, a later peak in the diurnal cycle of convective rainfall over land, and a reduced positive rainfall bias over the Indian Ocean. The simulations suggest that the reduced rainfall over the Indian Ocean leads to an enhanced monsoon circulation and transport of moisture into India. Increases in latent heating associated with increased convection over land deepen the monsoon trough and enhance water vapour transport into the continent. In addition, delayed continental convection allows greater surface insolation and, along with the same rain falling in more intense bursts, generates a drier land surface. This increases land-sea temperature contrasts, and further enhances onshore flow. Changes in the low-level water vapour advection into India are dominated by these changes to the flow, rather than to the moisture content in the flow. The results demonstrate the need to improve the representations of convection over both land and oceans to improve simulations of the monsoon.

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.

International audience; Observations made during the TWP-ICE campaign are used to drive and evaluate thirteen cloud-resolving model simulations with periodic lateral boundary conditions. The simulations employ 2D and 3D dynamics, one- and two-moment microphysics, several variations on large-scale forcing, and the use of observationally derived aerosol properties to prognose droplet numbers. When domain means are averaged over a 6-day active monsoon period, all simulations reproduce observed surface precipitation rate but not its structural distribution. Simulated fractional areas covered by convective and stratiform rain are uncorrelated with one another, and are both variably overpredicted by up to a factor of ∼2. Stratiform area fractions are strongly anticorrelated with outgoing longwave radiation (OLR) but are negligibly correlated with ice water path (IWP), indicating that ice spatial distribution controls OLR more than mean IWP. Overpredictions of OLR tend to be accompanied by underpredictions of reflected shortwave radiation (RSR). When there are two simulations differing only in microphysics scheme or large-scale forcing, the one with smaller stratiform area tends to exhibit greater OLR and lesser RSR by similar amounts. After ∼10 days, simulations reach a suppressed monsoon period with a wide range of mean precipitable water vapor, attributable in part to varying overprediction of cloud-modulated radiative flux divergence compared with observationally derived values. Differences across the simulation ensemble arise from multiple sources, including dynamics, microphysics, and radiation treatments. Close agreement of spatial and temporal averages with observations may not be expected, but the wide spreads of predicted stratiform fraction and anticorrelated OLR indicate a need for more rigorous observation-based evaluation of the underlying micro- and macrophysical properties of convective and stratiform structures.

International audience; The role of convective processes in moistening the atmosphere during suppressed periods of the suppressed phase of a Madden-Julian oscillation is investigated in cloud-resolving model (CRM) simulations, and the impact of moistening on the subsequent evolution of convection is assessed as part of a Global Energy and Water Cycle Experiment Cloud System Study (GCSS) intercomparison project. The ability of single-column model (SCM) versions of a number of state-of-the-art climate and numerical weather prediction models to capture these convective processes is also evaluated. During the suppressed periods, the CRMs are found to simulate a maximum moistening around 3 km, which is associated with a predominance of shallow convection. All SCMs produce adequate amounts of shallow convection during the suppressed periods, comparable to that seen in CRMs, but the relatively drier SCMs have higher precipitation rates than the relatively wetter SCMs and CRMs. The relatively drier SCMs dry, rather than moisten, the lower troposphere below the melting level. During the transition periods, convective processes act to moisten the atmosphere above the level at which mean advection changes from moistening to drying, despite an overall drying effect for the column. The SCMs capture some essence of this moistening at upper levels. A gradual transition from shallow to deep convection is simulated by the CRMs and the wetter SCMs during the transition periods, but the onset of deep convection is delayed in the drier SCMs. This results in lower precipitation rates for these SCMs during the active periods, although much better agreement exists between the models at this time.

The scale of investment in new technologies by learning organisations now generates e-learning projects whose long-term success is no longer independent of the character and structure of the host organisation. Organisations expect an efficiency that will allow their investment to generate impact beyond the lifetime of any individual project. To achieve this successfully requires an organisational shift in the processes and practices associated with an innovation. Linking process with practices opens the way for generating measurable and specified change in terms of human and organisational behaviour. This can be set in the context of a production or content life-cycle.The e-Learning Maturity Model (eMM) provides a first step in being able to identifyprocess and practice sets that may be associated with individual educational technological innovations, such as adopting a content management system (CMS). The question is how to approach managing the changes that adopting a CMS will bring? eMM can provide a framework to help identify stakeholders in both old and new practices, to identify and agree information flows and dependencies as well as ownership and use of artefacts and information and the scope of responsibilities. This approach, fully consistent with established socio-technical approaches to organisational management and driven by the capacity for shared understanding provided by high level modelling, provides a promising way of embedding new practices in e-learning. The HEA Pathfinder project at the University of Manchester builds on Manchester’s benchmarking pilot in which eMM was trialled under the constraints presented by UK Higher Education. This included its ability to assess high-volume data representing suborganisation level units (such as individual faculties) and specific organisational functions, rather than being based primarily on data generated by projects that are considered to be representative of an organisation. This ability to use eMM as a tailored tool provided the basis for its use in identifying and constructing new processes and information flows relevant to the introduction of new technologies.

International audience; An idealized case-study has been designed to investigate the modelling of the diurnal cycle of deep precipitating convection over land. A simulation of this case was performed by seven single-column models (SCMs) and three cloud-resolving models (CRMs). Within this framework, a quick onset of convective rainfall is found in most SCMs, consistent with the results from general-circulation models. In contrast, CRMs do not predict rainfall before noon. A joint analysis of the results provided by both types of model indicates that convection occurs too early in most SCMs, due to crude triggering criteria and quick onsets of convective precipitation. In the CRMs, the first clouds appear before noon, but surface rainfall is delayed by a few hours to several hours. This intermediate stage, missing in all SCMs except for one, is characterized by a gradual moistening of the free troposphere and an increase of cloud-top height. Later on, convective downdraughts efficiently cool and dry the boundary layer (BL) in the CRMs. This feature is also absent in most SCMs, which tend to adjust towards more unstable states, with moister (and often more cloudy) low levels and a drier free atmosphere. This common behaviour of most SCMs with respect to deep moist convective processes occurs even though each SCM simulates a different diurnal cycle of the BL and atmospheric stability. The scatter among the SCMs results from the wide variety of representations of BL turbulence and moist convection in these models. Greater consistency is found among the CRMs, despite some differences in their representation of the daytime BL growth, which are linked to their parametrizations of BL turbulence and/or resolution.