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Accuracy of short-term sea ice drift forecasts using a coupled ice-ocean model. | LitMetric

AI Article Synopsis

  • Forecasts of Arctic sea ice drift for summer 2014 were generated using the MIZMAS model, based on atmospheric data from CFSv2, and validated against drifting buoys and other observations.
  • The model showed promising results, with root mean square (RMS) errors for ice speed significantly lower than those using climatological data, indicating improved accuracy in predicting ice movement.
  • Adjustments to model parameters could optimize the predictions further, enhancing the likelihood of successfully tracking targets in sea ice with high-resolution images over both 24-hour and 48-hour forecasts.

Article Abstract

Arctic sea ice drift forecasts of 6 h-9 days for the summer of 2014 are generated using the Marginal Ice Zone Modeling and Assimilation System (MIZMAS); the model is driven by 6 h atmospheric forecasts from the Climate Forecast System (CFSv2). Forecast ice drift speed is compared to drifting buoys and other observational platforms. Forecast positions are compared with actual positions 24 h-8 days since forecast. Forecast results are further compared to those from the forecasts generated using an ice velocity climatology driven by multiyear integrations of the same model. The results are presented in the context of scheduling the acquisition of high-resolution images that need to follow buoys or scientific research platforms. RMS errors for ice speed are on the order of 5 km/d for 24-48 h since forecast using the sea ice model compared with 9 km/d using climatology. Predicted buoy position RMS errors are 6.3 km for 24 h and 14 km for 72 h since forecast. Model biases in ice speed and direction can be reduced by adjusting the air drag coefficient and water turning angle, but the adjustments do not affect verification statistics. This suggests that improved atmospheric forecast forcing may further reduce the forecast errors. The model remains skillful for 8 days. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10 km × 10 km image from 60 to 95% for a 24 h forecast and from 27 to 73% for a 48 h forecast.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070527PMC
http://dx.doi.org/10.1002/2015JC011273DOI Listing

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