A recent modeling study supported by CPO's Climate Observations and Monitoring Program was published in Environmental Research Letters on May 19. The study, led by PI Jiping Liu, is titled: "Revisiting the potential of melt pond fraction as a predictor for the seasonal Arctic sea ice extent minimum."
The rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. This modeling study employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May.
In the study, the researchers show that satellite observations show no evidence of predictive skill in May. However, they found that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July.
Results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state.
To view the full paper, visit: iopscience.iop.org/1748-9326/10/5/054017