MOCCHA Analysis of Dynamic, Cloud, and Aerosol Processes

The climate of the Arctic is changing rapidly, warming at least twice as fast as the global average. While climate models are generally consistent with this amplified warming, they show much greater variability between model in the Arctic than elsewhere, and have conspicuously failed to reproduce some key features of observed climate change - notably the rapid reduction in summer sea ice extent.
The greatest source of uncertainty in models of Arctic climate, indeed for climate in general, is clouds. Clouds are the dominant control of the surface energy budget through their impact on both solar and infra red radiation. The effect of clouds on the surface radiation budget is commonly expressed as the 'cloud radiative effect' (CRE): the difference between the radiation under clear skies and with clouds present. Results from the latest Climate Model Intercomparison Project (CMIP5) show that the annual mean CRE of all the models is biased by -10 W m-2 with a standard deviation across the models of similar magnitude. The bias for the summer months only is +20 W m-2. A bias of just 1 W m-2 over a year is sufficient to melt/freeze a 10 cm thick layer of sea ice; these model biases are thus highly significant.
We aim to reduce the bias and uncertainty resulting from model summertime clouds in the Arctic, by improving understanding and parameterization of key processes affecting their formation, development, and dissipation. Arctic summer boundary-layer clouds are mixed-phase - containing both liquid water and ice crystals. This is a thermodynamically unstable non-equilibrium state because saturation water vapour pressure over ice is lower than that over water, thus ice crystals should grow at the expense of water droplets. The clouds are maintained in a quasi-equilbrium through a complex set of interactions between aerosol (on which droplets and ice crystals form), thermodynamic, microphysical, radiative, and turbulent mixing processes. All of these individual processes have significant uncertainties associate with them, and current model parameterizations have been developed almost entirely based on measurements in mid-latitude clouds where conditions are very different.
We will use a combination of state-of-the-art measurements of cloud and boundary layer properties and processes, with detailed modelling studies using the Met Office Unified Model. The measurements utilise high resolution active remote sensing systems such as cloud radar and lidar, processed through the CLOUDNET algorithm to generate detail cloud property data. These are supplemented with in situ measurements of boundary layer structure and aerosol properties. Analysis of combined model and observational data will be used to identify key failings in model parameterizations, and new understanding of the physics that must be represented in the models. An iterative process of parameterization updates and testing against observations will be used to improve the model physics. The updates will then be tested in 20-year climate model runs using cases from the Atmospheric Model Intercomparison Project (AMIP) to evaluate their impact on climate projections. The Unified Model is chosen as an ideal tool because of its seamless philosophy, using the same model physics in both operational forecast and climate models. This allows direct testing against observational cases, and easy transfer of new model updates to the climate version. The final model updates will then be put forward to the Met Office for integration into the official model releases.

The primary impact of this project will be improved climate projections and operational forecasts for the Arctic region, initially through the UK Met Office. We use the Met Office model as our test and development environment because of it's unique common framework for operational and climate versions. This allows our primary outputs - new and improved parameterizations of Arctic boundary layer and cloud processes - to be implemented readily in both versions of the model.
Improved operational forecasts are a direct benefit for increasing commercial activities in the Arctic, notably shipping during the summer months. Similar benefits should apply to seasonal forecasts. On climate timescale we should enable a reduction in uncertainty arising from cloud processes in the Arctic summer, and more accurate projections of future climate, including the potential mid-latitude impacts resulting from changing gradients between the Arctic and lower latitudes.

The CLOUDNET data products from the project will also be made freely available by the end of the project. These are readily used by other models, including those of many EU forecast agencies.