Tropospheric ozone is an important air pollutant, harmful to human health, agricultural crops and vegetation. It is the main precursor to the atmospheric oxidants which initiate the degradation of most reactive gases emitted to the atmosphere, and is an important greenhouse gas in its own right. As a consequence of this central role in atmospheric chemistry and air pollution, the capacity to understand, predict and manage tropospheric ozone levels is a key goal for atmospheric science research. This goal is hard to achieve, as ozone is a secondary pollutant, formed in the atmosphere from the complex oxidation of VOCs in the presence of NOx and sunlight, and the timescale of ozone production is such that a combination of in situ chemical processes, deposition and transport govern ozone levels. Uncertainties in all of these factors affect the accuracy of numerical models used to predict current and future ozone levels, and so hinder development of optimal air quality policies to mitigate ozone exposure. Here, we will address this problem by measuring the local chemical ozone production rate, and (for the first time) perform measurements of the response of the local atmospheric ozone production rate to NOx and VOC levels - directly determining the ozone production regime. We will achieve this aim by building upon an existing instrument for the measurement of atmospheric ozone production rates (funded through a NERC Technology Proof-of-Concept grant, and deployed in the recent ClearfLo "Clean Air for London" NERC Urban Atmospheric Science programme). In addition to directly measuring ozone production, by perturbing the ambient chemical conditions (for example, through addition of NOx or VOCs to the sampled airflow), and measuring the effect of this change upon the measured ozone production rate, the ozone control regime (extent of NOx vs VOC limitation) may be directly determined. Within this project, we will develop our existing ozone production instrument to include this capability, and validate the measurements, through comparison with ozone production from VOC oxidation in a large simulation chamber, and by measurement of the key oxidant OH radicals, and their precursors, within the system. We will then apply the instrument to compare the measured ozone production rates with those calculated using other observational and model approaches, and to characterise the ozone control regime, in two contrasting environments: In the outflow of a European megacity (at Weybourne Atmospheric Observatory, WAO, in the UK), and in a rural continental location (at Hohenpeissenberg, HPB, in southern Germany). At WAO, we will compare the measured ozone production rate with that calculated through co-located measurements of HO2 and RO2 radicals (using a newly developed approach to distinguish between these closely related species), and with that simulated using a constrained photochemical box model. We will compare the NOx-dependence of the ozone production rate with that predicted using indicator approaches, based upon observations of other chemical species. At HPB, we will focus upon the VOC-dependence of the ozone production rate, and assess the error in model predictions of ozone production, which arise from the presence of unmeasured VOCs. The project will develop and demonstrate a new measurement approach, and apply this to improve our understanding of a fundamental aspect of atmospheric chemical processing. Future applications have considerable potential both to support atmospheric science research, but also as an important air quality tool, alongside existing measurement and modelling approaches, to inform the most effective emission controls to reduce ozone production in a given location. In the context of global crop yield reductions arising from ozone exposure of 7 - 12 % (wheat), 6 - 16 % (soybean) and 3 - 4 % (rice), this is an important societal as well as scientific goal.

Approximately 4 million properties in the UK are at risk from surface-water flooding which occurs when heavy rainfall overwhelms the drainage capacity of the local area. In the future, as a result of climate change, the frequency and intensity of severe weather events, such as storms and floods, is likely to increase. Accurate forecasts of severe weather provide significant benefit, allowing households and businesses to take mitigating action and emergency services to mobilize resources. Numerical weather forecasts are obtained by evolving forward the current atmospheric state using computational techniques that solve equations describing atmospheric motions and other physical processes. The current atmospheric state is estimated by a sophisticated mathematical technique known as data assimilation. Data assimilation blends previous forecasts with new atmospheric observations, weighted by their respected uncertainties. The uncertainty in the observations is not well understood, and currently up to 80% of observations are not used in the assimilation because these uncertainties cannnot be properly quantified and accounted for. Working in partnership with the UK Met Office, we have recently demonstrated in the NERC FRANC: Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection project (NE/K008900/1), that it is now feasible to estimate spatial statistics for observation uncertainty. Our previous work in idealized systems has shown that better accounting for these errors in the assimilation is expected to provide significant forecast improvement. There are still a number of fundamental questions to address before the benefits can be realized in operational forecasts. This proposal to the NERC International Opportunities fund will add value to the work carried out in FRANC, by supporting access to international observation data, numerical weather prediction models and assimilation systems. We will build a new collaboration with the Deutscher Wetterdienst (German Weather Service), and compare observation error statistics for Doppler radar wind data from Deutscher Wetterdienst with those from the UK Met Office. By considering the similarities and differences between the operational forecasting systems, and attributing these to features in the observation error statistics, we will obtain a detailed knowledge of the error sources. By carrying out theoretical and idealized studies and comparing their results with the statistics from the operational systems, we will gain understanding of the impact of differences in the assimilation systems on the diagnostic used to estimate the observation error statistics. In turn, this should allow the observation errors to be reduced, and therefore more of the expensively acquired observations to be utilised, rather than discarded. Ultimately, understanding the observation uncertainty will result in improved forecasts of severe weather events.

This project, studying Convective Cloud Dynamics and Turbulence Interactions with Microphysical Processes and the Atmospheric Environment (CLOUDY TIME) will: (i) improve understanding of microphysics-turbulence interactions using a hierarchy of sub-km models and large-eddy simulations; (ii) evaluate the 3D representation of moist convective turbulence in sub-km and km-scale models, testing turbulence parametrization schemes including coupling with microphysics; (iii) improve understanding of model uncertainty due to representation of vertical profiles; and (iv) evaluate mesoscale processes that lead to cloud organisation to inform scale-aware convection parametrization schemes. The improved understanding and evaluation in CLOUDY TIME will be informed by novel measurements and observations planned for the UK summertime convection field campaign WesCon, which aims to observe many of the relevant turbulent processes, and their relation to the environment, for the first time. Convection leads to hazardous weather and is fundamental to the global atmospheric circulation. Modelling of convective storms is challenging due to the interaction of many processes which interact over a wide range of scales, from turbulence and microphysics, including precipitation formation, to the release of convective instability and evaporatively driven downdraughts and cold pools. The next generation of global weather and climate models will be run at km-scale grid lengths and will explicitly represent convective storms, but these models are highly sensitive to the sub-grid turbulence parametrization, even when run at finer resolutions with grid lengths less than 1 km. This sensitivity leads to biases in storm number, intensity and lifetime, and hence to errors in severe weather warnings and in the large-scale circulation. Conversely, errors on the large scale affect the timing and nature of convection, creating a complex web of interactions across scales. CLOUDY TIME aims to disentangle the controls on convection from the microscale, governed by parametrization, to the synoptic scale, governed by data assimilation and downscaling.

Aviation contributes to about 5% of the total anthropogenic climate change when including non-CO2 effects, e.g., contrail formation and the impact of NOx emissions on ozone and methane. Among various non-CO2 effects, the contrail-cirrus radiative forcing is the largest (~2/3) with large uncertainties. The most critical affecting factor is the huge weather-induced variability of the radiative impact of individual contrails. This is the quantity, BeCoM will predict better since the knowledge of the individual radiative forcing is the basis for avoidance of just those contrails that contribute most to the overall climate impact. Once this is standard, it will be possible to formulate adequate mitigation measures and develop policy-driven implementation schemes. BeCoM will address the uncertainties related to the forecasting of persistent contrails and their weather-dependent individual radiative effects. BeCoM focuses on: 1) obtaining a larger and higher resolution database of relative humidity and ice supersaturation at cruise levels for assimilation into numerical weather prediction (NWP) models; 2) providing more adequate representation of ice clouds in their supersaturated environment in the NWP models; and 3) validation of the predictions to determine and reduce the remaining uncertainties of contrail forecasts. To facilitate the assimilation and validation process, BeCoM will develop a novel hybrid artificial intelligence algorithm. Based on the contrail prediction, BeCoM will develop a policy framework for effective contrail avoidance through a trajectory optimization approach. BeCoM will enable a better understanding of contrails climate impact and formulate recommendations on how to implement strategies to enable air traffic management to reduce aviation's climate impact. The BeCoM consortium builds on its knowledge and expertise covering a wide spectrum from atmospheric science and climate research to aviation operations research and policy development.

Many of the clouds in the atmosphere contain ice particles. These ice particles play an important role in the climate system, because high-altitude cirrus clouds cover around 30% of the globe at any one time, and act to warm the planet. Ice particles are also important for the development of precipitation, and not only in cold polar climates: even in mid-latitudes (like the UK), over three quarters of the precipitation that falls originates as snowflakes aloft - it is just that most of it melts before arriving at the surface. Ice particles, both in clouds and in snowfall at the surface, precipitate. In other words, they are able to grow large enough to fall through the air. This has several implications. The most obvious of these is that the rate at which the particles fall out controls the transport of water vertically through the atmosphere and to the surface. More subtly, the movement of each ice particle through the air directly influences the rate at which the particle grows, evaporates and melts. For example, if the air is humid enough, water molecules will diffuse to the ice crystal's surface and deposit there, leading to growth. If the particle is stationary, this growth occurs steadily but slowly, because the growing ice crystal depletes the vapour around it, leading to a shallow gradient in the concentration of molecules. If the particle is falling, this growth can occur much faster, because the ice crystal is constantly falling into fresh, humid air, leading to steep concentration gradients. The quantitative details of exactly how fast an ice particle of a given size and shape falls, and how much the growth rates are enhanced by, is determined by the airflow around the ice particle, or its aerodynamics. Unfortunately, this is an area of cloud physics where our understanding is extremely limited. The aerodynamics of simple shapes like spheres, spheroids and discs is well studied. However it is clear from observation of natural ice particles that they are not simple in their geometry. Instead the particles are often complex and irregular in their shape. We have almost no high-quality data on the aerodynamics of such particles. As a result, even state-of-the-art microphysical models are forced to approximate the aerodynamical effects on ice processes as though these complex irregular particles were spheres or spheroids, hoping that this is an adequate approximation. To solve this problem, experimental data is needed for the aerodynamics of particles with the complex shapes that we observe in the atmosphere. The stumbling block is that making suitable observations of natural ice particles in free-fall is extremely challenging. In snowfall at the surface the particles are small, fragile, easily blown by the wind, and likely to melt or evaporate if not handled with great care. Direct sampling of falling particles in cirrus clouds is impossible. In neither case is it possible to directly determine the airflow around the particle or the influence of that flow on the microphysical process rates. In this project we overcome these problems with the use of analogues. Using 3D printing techniques we will create plastic particles with the same complex geometry as natural ice particles. By dropping the particles in tanks of liquids, and through air in the laboratory and a vertical wind tunnel, we can determine how the fall speed of the particles is controlled by their size and geometry. Exploiting recent developments in tomographic particle imaging velocimetry we can measure the airflow around the falling analogues. From this we can directly determine how the airflow enhances the particle growth, evaporation and melting rates.