There is currently a large effort in the development of general circulation model (GCM)-based seasonal to decadal prediction systems to provide climate forecasts. Such techniques are rather complex, technically challenging and still in their infancy. Any weather or climate forecast will be subject to three sources of uncertainty, namely observation uncertainty, the model-component of initial uncertainty, and model uncertainty over the forecast period. The aim of this proposal is to improve the reliability of extended range forecast of weather and climate, mainly focusing on the ocean component of the coupled system. We propose to develop and incorporate various tools based on stochastic physics to improve the reliability of forecasts focusing on a more accurate representation of ocean observations and model uncertainties. The individual impacts of the different developments on the reliability of the forecasts will be quantified to provide estimates of the different sources of uncertainties in the forecasts. The development of reliable extended range forecasts can be extremely beneficial with major economical and societal consequences.

Imagine a severe weather event occurs, causing devastating impacts to a particular region. One question that is repeatedly asked to climate scientists by politicians, disaster responders, recovery planners and journalists is about the role of climate change in causing or affecting the event. The direct cause of the devastation is the unusual weather but, in many cases, climate change will have made the event more likely, more severe, or potentially both. In those cases, the devastation may be partly or even mostly due to the change in climate. In some cases, the worst consequences may be due to the vulnerability of those living in the region, or a combination of many different factors which will reflect past and current decisions on a variety of levels. Understanding whether climate change has made the event more damaging is important. Wealthier nations have caused the world to warm, but poorer nations have experienced some of the most damaging consequences. International climate negotiations are discussing the issue of 'loss and damage' - whether and how those mainly responsible for climate change should compensate those who experience the worst consequences. This project will aid those discussions by providing answers to key questions about how the consequences of extreme weather events have already changed and how those consequences may change further in future, and by placing those events within their specific contexts of vulnerability. We will develop a new methodology to answer questions about the severity of extreme weather events - how have the consequences of a particular weather situation been made worse by climate change? If the same weather situation had occurred in the climate that we had 100years ago, would it have been less damaging? What about if the weather situation happens again in the future? These are well-defined questions, but we cannot easily answer them yet. As an example, we might expect that more rain would fall today in a severe storm than if the same storm had occurred 100 years ago, potentially making the consequences worse. But, how much more rain? And, beyond the direct meteorological consequences, what about the effects on river flows and people? We will also use these same concepts in reverse by applying them to extreme events that occurred several decades ago to examine how their consequences would be different today in a warmer world. This project will consider many different types of extreme weather event, including heavy rainfall, windstorms, heatwaves and droughts, and examine the consequences of those weather events for society, including damage to property and flooding. Importantly, we will identify the additional impacts of a particular weather event which are due to living in a warmer world, directly addressing the critical issue of losses and damages caused by climate change. We will also build narratives of plausible worst-case events to inform decision making on adapting to our warming world.

Biomass burning (BB) and wildfires release huge quantities of particulates and trace gases into the atmosphere in amounts highly variable in space and time. Plume rise means these that under certain conditions these emissions can be injected into the atmosphere at heights far above the Earth surface, enhancing their long-range transport and altering their atmospheric chemistry, radiative budget, and air quality effects. Results from past project show that UK air quality can be signficantly affected by long-range transport of smoke from European and Russian wildires, and smoke from fires in Canada can be detected in air samples at DEFRA monitoring stations in e.g. Mace Head. Near real-time (NRT) atmospheric modelling and forecasting schemes aiming to realistically represent these aspects of the Earth system must include a high temporal resolution, non-retrospective source of BB emissions information - which generally comes from satellite Earth Obervation data. However, as discussed above, a fires smoke plumes buoyancy characteristics can strongly influence its atmospheric impact, and this is increasingly realised to be an important term to represent when modelling the long-range effects of wildfire smoke emissions. However, a lack of a priori information and, until recently, a directly-related EO observable, has meant that parameterisation of smoke plume injection height has received far less attention than has estimating the magnitude and variability of the smoke emissions. This KE Project will exploit the findings from two successful NERC research projects to provide major improvements to the current (ad hoc) prescription of wildfire smoke plume injection height in the prototype GMES UK/European atmospheric monitoring and forecasting scheme (the 'GMES Atmospheric Core Service', which is based on the world-leading integrated forecast system (IFS) of ECMWF in the UK and which is being desiged to provide the public, policy makers and downstream organisations with access to state-of-the-art atmospheric chemistry monitoring and forecasting data. The GACS serves a broad community of users, for example those involved in environmental policy development and policing, those delivering downstream services related to the health community (warning of increased asthma incidence during air pollution episodes), and those aiming to reduce public exposure to air pollution. We will work with Project Partners developing the GACS to exploit the research on plume height rise developed in NE/E016863/1 and the EO data processing procedures developed in NE/H00419X/1 to provide a much more realistic representation of smoke injection height in the GACS system; one that takes account of both fire and atmospheric characteristics such that the atmospheric transport of these emissions, including to the UK, can be better represented. The Project Partners are ECMWF, who lead GACS development in the UK and who operate the global model within which the plume rise scheme will be embedded, and Jülich Research Centre who are experts in the chemistry and transport of smoke emissions and who are a main partner in the GACS development. The KCL Environmental Research Group (KCL-ERG) are a 'down-stream' user of global atmospheric model output, funded by UK Government to provide regional air quality (AQ) monitoring and modelling, and this KE project will support them in starting to use the enhanced GACS outputs in their UK regional and London-wide AQ modelling schemes, in particular to take better take account of smoke-polluted air that is known to move into the UK from e.g. eastern Europe or western Russia, and which at present causes enhanced discrepancies between the AQ models and measurements (see DEFRA letter of support). All model outputs incorporating the new scheme will be made available freely through the GMES GACS system interface http://www.gmes-atmosphere.eu/ and for the UK region throught the online public interface www.londonair.org.uk/

Data assimilation is a method to combine numerical models with observations. It is used in all environmental sciences and essential to be able to simulate the real world, instead of a pure model world which has little to do with reality. With the increasing resolution of geophysical models both the size and the nonlinearity of these models increase. Also the number of observations increases and the observation operators, which connect the model variables to the observations, become more and more complex and nonlinear, like new satellite observations and radar observations in weather forecasting. Obviously, the data-assimilation methods have to fully allow for these nonlinearities. Present-day implementations in numerical weather prediction are all based in linearisations. For example, the (Ensemble) Kalman Filter assumes linear updates, and variational methods like 4DVar solve a weakly nonlinear problem through linear iterations. A further problem with variational methods is that error estimates are hard to obtain, and for highly nonlinear problems inaccurate. A few operational weather prediction centres have started experimenting with ensembles of 4DVar's. This has the potential of solving the nonlinearity problem, and at the same time provides an error estimate. Recently, the European Centre for Medium Range Weather Forecasts (ECMWF) started experimenting with ensembles of 4DVar solutions, generated by perturbing the observations, with very promising results. It is known from Kalman Filter (or rather Smoother) theory that when this ensemble is cycled through several data-assimilation cycles its spread will approximate the error covariance of the system. In that case, the ensemble is a sample from the correct distribution. However, for a strongly nonlinear system the Kalman filter theory does not hold, and it is unclear what the ensemble means, and there is a strong scientific and operational need to understand what these ensembles mean, and how we can improve them. On the other hand, it is well-known that we can represent the underlying distributions by a set of particles, i.e. a set of model states, in a so-called particle filter. Particle filters are fully nonlinear both in model evolution and analysis step. A fundamental problem, the so-called 'curse of dimensionality' has hampered their use in geoscience applications. Very recently a solution has been found by the PI that has the potential to revolutionize data assimilation in highly nonlinear geophysical systems (Van Leeuwen, 2010a; Van Leeuwen, 2010b). The latter paper describes applications to relatively simple (up to 1000-dimensional) highly nonlinear systems that previously needed hundreds to thousands of model integrations, and now only of the order of 20 model integrations. This research proposal explores the possibilities of combining 4DVar ensembles with ideas from Particle Filtering for the next generation numerical weather prediction. A simple and exciting idea is to use 4DVar solutions as particles in the Particle Filter, and this is one of the directions we will investigate. But we will also investigate other ways to generate 4DVar ensembles that are meaningful in nonlinear systems. A strong point is that we will have direct access to the operational ECMWF system, allowing us to efficiently operate between relatively simple academic models and the operational world.

The vast ocean surface populated by wind-generated waves is where atmosphere and ocean interact. It is also where maritime and offshore renewable energy industries have to operate to secure future sustainable energy. Wave breaking provides the upper limit to how large waves may become and is the mechanism for how they dissipate energy. Breaking in crossing seas, the harshest conditions for shipping and offshore renewable energy (ORE) design, is poorly understood. A recent case study of the famous Draupner rogue wave by four of the investigators (M.L. McAllister et al. (2019) Laboratory recreation of the Draupner wave and the role of breaking in crossing seas. J. Fluid Mech. 860, 767-786) has shown that breaking in such seas is fundamentally different: it limits maximum wave height much less and is potentially much less dissipative. As a consequence, existing breaking criteria as implemented in wave forecasting tools and offshore design guidelines are not valid and unreliable in crossing seas. Through collaboration with DNV GL (an international accredited registrar and classification society), the European Centre for Medium-Range Weather Forecasts (a world-leading operational wave forecasting agency) and Shanghai Jiao Tong University (ranked first in the world for ocean engineering in the Shanghai Ranking), this project aims to develop and experimentally and numerically validate robust new wave breaking and dissipation criteria appropriate for highly directionally spread and crossing-sea conditions and implement these in wave forecasting tools and offshore design guidelines. The UK and Ireland possess substantial offshore wind resources that are capable of making major contributions to their national and international energy supply. A key problem in developing such resources is designing against the harsh ocean environment that prevails in the territorial waters of both countries. The design challenge is even greater in China (with an estimated 100bn offshore wind market), where candidate sites for offshore wind farms are exposed to typhoons, in which crossing sea conditions have an increased likelihood. The proposal will address this challenge through extensive large-scale experiments in two globally unique wave, FloWave at the University of Edinburgh (part of the UK ORE testing infrastructure) and the Ocean Basin at Shanghai Jiao Tong University, state-of-the-art numerical simulations and the development of new theory. The 30-month proposal has an investigating team across four universities (Oxford, Edinburgh, Manchester and University College Dublin) consisting of a PI, four Co-Is and a Researcher Co-I, three of whom are early-career researchers.