The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1
- 1NCAS-Climate, Department of Meteorology, University of Reading, Reading, UK
- 2European Centre for Medium-Range Weather Forecasts, Reading, UK
- 3College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- 4Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
- 5Institut Català de Ciències del Clima, Barcelona, Spain
- 6CNRM/GAME, Toulouse, France
- 7British Atmospheric Data Centre, Rutherford Appleton Laboratory, Chilton, UK
- 8Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
- 9Meteorological Research Institute, Tsukuba, Japan
- 10Max Planck Institute for Meteorology, Hamburg, Germany
- 11Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, Canada
- 12Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
Abstract. Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state.
The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric.
Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation.