Aspects of designing and evaluating seasonal‐to‐interannual Arctic sea‐ice prediction systems

Abstract

Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter-annual Arctic sea-ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems.

Predictability of the Arctic sea ice edge

“…To assess whether forecast improvements are in principle possible, the inherent limits of predictability have been investigated in a number of studies based on individual models [ Koenigk and Mikolajewicz , ; Blanchard‐Wrigglesworth et al , ; Holland et al , ; Tietsche et al , ; Day et al , ]. Recently, several global climate modeling groups have conducted idealized experiments following a common protocol developed for the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project [ Tietsche et al , ; Day et al , ; Hawkins et al , ]. Under the assumption that the predictability of Arctic sea ice in the real world is captured by the set of models used, these “perfect‐model” studies reveal that pan‐Arctic sea ice extent and volume could be predicted much more skillfully than is currently possible with existing prediction systems [ Stroeve et al , ; Blanchard‐Wrigglesworth et al , ].…”

Seasonal differences in the response of Arctic cyclones to climate change in CESM1

“…Due to the chaotic nature of the climate system, these perturbations grow until initial condition memory is lost creating spread among the ensemble members. Although the exact method of perturbing the initial conditions can affect the spread early in the simulation (Hawkins et al 2016), a few decades into the simulation the climate state space will be well sampled by the ensemble, since the limit of initial-value predictability for surface ocean and atmospheric variables is about a decade (e.g. Collins et al 2006;Branstator and Teng 2010).…”

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

“…Boer et al, 2013;Hawkins et al, 2016). To the extent that the model (or multimodel combination) successfully reproduces climate system behaviour, predictability results indicate where geographically, and for which variables, there may be the possibility of improving the forecast system.…”

“…It is important that the year1 to year2 period is the same for all forecast ranges in order to provide consistent estimates (Hawkins et al, 2014) and avoid difficulties in interpreting forecasts relative to different baselines (Smith et al, 2013b). We recommend taking year1 as 1970 and year2 as 2016 for the DCPP Component A hindcasts that are part of CMIP6.…”