climate model – physical and statistical aspects, in preparation

Ensemble predictions with MPI-ESM under the perfect-model assumption give upper bounds on predictability of large Arctic sea-ice anomalies in RCP45 runs: 1. It is easy to beat a climatology forecast → lead times less than two years (summer) to three years (annual mean) 2. It is hard to beat a damped...

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Bibliographic Details
Main Authors: S. Tietsche, D. Notz, J. H. Jungclaus, J. Marotzke, M. Ogi, I. G. Rigor, M. G. Mcphee, J. M. Wallace, Marika M. Holl, Cecilia M. Bitz, Bruno Tremblay
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Arctic
Sea ice
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.676.5398
http://conference2011.wcrp-climate.org/posters/C40/C40_Tietsche_Th119A_0.pdf
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Summary:Ensemble predictions with MPI-ESM under the perfect-model assumption give upper bounds on predictability of large Arctic sea-ice anomalies in RCP45 runs: 1. It is easy to beat a climatology forecast → lead times less than two years (summer) to three years (annual mean) 2. It is hard to beat a damped persistence forecast → lead times less than a few months (summer) to two years (annual mean) 3. For sub-annual lead times, sea-ice-assimilated initial conditions are competitive with lagged-perfect initial conditions Although sea-ice anomalies will be large in the future, there is only moderate prospect of predicting them in advance. 5. Discussion & Outlook Next step: find physical processes that determine predictability I Fast atmospheric processes ↔ low predictability, e.g. Arctic surface wind stress in summer [2] ISlow oceanic processes ↔ higher predictability, e.g. ocean heat transport into Arctic [3]

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