Fluctuations in the boundary region of the Alcator C-Mod tokamak have been analyzed using gas puff imaging data. It is found that the fluctuation amplitudes in the near scrape-off layer follow a normal distribution while the far scrape-off layer fluctuations are dominated by large amplitude bursts due to radial motion of blob-like structures and have a positively skewed and flattened amplitude probability distribution. Conditional averaging of the time series reveals burst wave forms with a fast rise and slow decay and exponentially distributed burst amplitudes and waiting times. Based on this, a stochastic model of the burst dynamics is constructed. The model predicts that fluctuation amplitudes should follow a Gamma distribution and that there is a parabolic relation between the skewness and the kurtosis moments of the fluctuations. This is shown to compare favorably with the gas puff imaging data over a range of line-averaged plasma densities.

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean–sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, but possibly even longer.

Increasing ship traffic and human activity in the Arctic has led to a growing demand for accurate Arctic weather forecast. High-quality forecasts obtained by models are dependent on accurate initial states achieved by assimilation of observations. In this study, a multi-variate nudging (MVN) method for assimilation of sea-ice variables is introduced. The MVN assimilation method includes procedures for multivariate update of sea-ice volume and concentration, and for extrapolation of observational information spatially. The MVN assimilation scheme is compared with the Ensemble Kalman Filter (EnKF) using the Los Alamos Sea Ice Model. Two multi-variate experiments are conducted: in the first experiment, sea-ice thickness from the European Space Agency's Soil Moisture and Ocean Salinity mission is assimilated, and in the second experiment, sea-ice concentration from the ocean and Sea Ice Satellite Application Facility is assimilated. The multivariate effects are cross-validated by comparing the model with non-assimilated observations. It is found that the simple and computationally cheap MVN method shows comparable skills to the more complicated and expensive EnKF method for multivariate update. In addition, we show that when few observations are available, the MVN method is a significant model improvement compared to the version based on one-dimensional sea-ice concentration assimilation.

• Both dynamical and machine learning methods are applied for sea-ice modelling • We demonstrate the potential of machine learning in sea-ice forecasting • The dynamical model utilises data assimilation of high-resolution sea-ice concentration and sea-surface temperature satellite observations

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining a best possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, these include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean-sea-ice model is used to asses the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application facility, thin sea ice thickness from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice thickness from ESA’s CryoSat satellite, and a new snow depth product derived from the National Space Agency’s Advanced Microwave Scanning Radiometers (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a positive effect on snow thickness and ice concentration. In our study, the seasonal forecast showed that assimilating snow depth lead to a worse estimation of sea-ice extent compared to the other assimilation systems, the other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, possibly even longer.