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Naval Research Lab Monterey

1 Projects, page 1 of 1
  • Funder: UKRI Project Code: NE/T006773/1
    Funder Contribution: 388,926 GBP
    Partners: Met Office, University of Reading, MET, ECMWF, Naval Research Lab Monterey, OCU

    As climate has warmed in response to increasing greenhouse gases, the distribution of Arctic sea ice has changed dramatically, becoming thinner over large portions of the Arctic Ocean basin in summer with a prominent reduction of the September minimum in sea ice extent. Human activity is increasing within the Arctic as the environment changes, with more residents and visitors making use of the increased window for shipping, offshore operations and tourism during summer. This has driven demand for coupled forecasts of weather, ocean and sea-ice state across the Arctic on the timescales needed to make risk-based decisions. Weather forecast skill for the Arctic is lower than for northern mid-latitudes, but the reasons why are multi-faceted and not fully known. Our hypothesis is that some aspects of the Arctic environment are not well forecast because the surface conditions beneath Arctic weather systems are more dynamic due to the movement of sea ice. Understanding of the physical processes that couple the atmosphere, ocean and sea ice is incomplete and the new frontier in prediction is to model this coupled system with fidelity and skill. Centres striving to improve capability in this area are our project partners: the Met Office, ECMWF and Met Norway. Arctic cyclones are the dominant type of hazardous weather system affecting the Arctic environment in summer - thus a concern for all human activities. They can also have critical impacts on the Arctic environment: in particular on sea-ice movement, sometimes resulting in 'Very Rapid Ice Loss Events' (VRILEs - timescale days to weeks) which present a major challenge to coupled forecasts; and on the baroclinicity (temperature gradients) around the Arctic, influencing subsequent weather systems and forecasts of Arctic climate from weeks out to a season ahead. Our proposed observational experiment will be the first focusing on summer-time Arctic cyclones and taking the measurements required to investigate the influence of sea-ice conditions on their development. New observations are needed comprising of turbulent near-surface fluxes of momentum, heat and moisture measured simultaneously with the sea ice or ocean surface beneath the aircraft track and along cyclone-scale transects. These fluxes dictate the impact of the surface on the development of weather systems. We will operate from Svalbard (Norway) in summer 2021, using the British Antarctic Survey's Twin Otter low-flying aircraft equipped to measure turbulence at flight level and the surface properties through infrared and lidar remote sensing. Our US partners, have designed an observational experiment, called THINICE, looking downwards on Arctic cyclone structure from an aircraft flying above the tropopause (10 km). Our projects are co-designed for summer 2021 so that the observations from the Twin Otter will form a bridge between US airborne and satellite measurements above and the properties of the surface fluxes and sea ice beneath. The project brings together expertise in observations, modelling and theoretical approaches to surface exchange, cyclone dynamics and sea-ice physics. We will use novel theoretically-based approaches to interrogate forecast models as they run and determine the mechanisms through which the surface properties alter cyclone growth. The new surface and turbulence data will be used to improve the parametrization of form drag in models that is central to wind forcing of sea-ice motion as well as decelerating surface winds. These aspects will be explored with state-of-the-art atmosphere and sea-ice dynamics models. Finally, we will close the loop through investigation of the effects of increased surface roughness on Arctic cyclones and their coupled interaction with Arctic temperature gradients. A major legacy of the project will be the unprecedented observations that will enable much needed evaluation and development of environmental forecast models for decades to come.