Predictability of large interannual Arctic sea-ice anomalies

In projections of twenty-first century climate, Arctic sea ice declines and at the same time exhibits strong interannual anomalies. Here, we investigate the potential to predict these strong sea-ice anomalies under a perfect-model assumption, using the Max-Planck-Institute Earth System Model in the same setup as in the Coupled Model Intercomparison Project Phase 5 (CMIP5). We study two cases of strong negative sea-ice anomalies: a 5-year-long anomaly for present-day conditions, and a 10-year-long anomaly for conditions projected for the middle of the twenty-first century. We treat these anomalies in the CMIP5 projections as the truth, and use exactly the same model configuration for predictions of this synthetic truth. We start ensemble predictions at different times during the anomalies, considering lagged-perfect and sea-ice-assimilated initial conditions. We find that the onset and amplitude of the interannual anomalies are not predictable. However, the further deepening of the anomaly can be predicted for typically 1 year lead time if predictions start after the onset but before the maximal amplitude of the anomaly. The magnitude of an extremely low summer sea-ice minimum is hard to predict: the skill of the prediction ensemble is not better than a damped-persistence forecast for lead times of more than a few months, and is not better than a climatology forecast for lead times of two or more years. Predictions of the present-day anomaly are more skillful than predictions of the mid-century anomaly. Predictions using sea-ice-assimilated initial conditions are competitive with those using lagged-perfect initial conditions for lead times of a year or less, but yield degraded skill for longer lead times. The results presented here suggest that there is limited prospect of predicting the large interannual sea-ice anomalies expected to occur throughout the twenty-first century

Snow in the changing sea-ice systems

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow–ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow–ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments

Prediction from Weeks to Decades

This white paper is a synthesis of several recent workshops, reports and published literature on monthly to decadal climate prediction. The intent is to document: (i) the scientific basis for prediction from weeks to decades; (ii) current capabilities; and (iii) outstanding challenges. In terms of the scientific basis we described the various sources of predictability, e.g., the Madden Jullian Ocillation (MJO); Sudden Stratospheric Warmings; Annular Modes; El Niño and the Southern Oscillation (ENSO); Indian Ocean Dipole (IOD); Atlantic “Niño;” Atlantic gradient pattern; snow cover anomalies, soil moisture anomalies; sea-ice anomalies; Pacific Decadal Variability (PDV); Atlantic Multi-Decadal Variability (AMV); trend among others. Some of the outstanding challenges include how to evaluate and validate prediction systems, how to improve models and prediction systems (e.g., observations, data assimilation systems, ensemble strategies), the development of seamless prediction systems