Using statistical downscaling to assess skill of decadal predictions

Abstract

We test a hypothesis that the signal-to-noise ratio of decadal forecasts improves with larger scales and can be utilised through model output statistics (MOS) involving empirical-statistical downscaling and dependencies to large-scale conditions. Here, we used MOS applied to an ensemble of decadal forecasts to predict local wet-day frequency, the wet-day mean precipitation, and the mean temperature for one to nine year long intervals of forecasts with a one year lead time. Our study involved a set of decadal forecasts over the 1961–2011 period, based on a global coupled ocean-atmosphere model that was downscaled and analysed for the North Atlantic region. The MOS for the decadal forecasts failed to identify aspects that were associated with higher skill for temperature and precipitation over Europe on time scales shorter than five years. A likely explanation for not enhancing skillful parts of the forecasts was that the raw model output had low skill for the general large-scale atmospheric circulation. This was particularly true for the wet-day frequency over Europe, which had a strong connection to the mean sea-level pressure (SLP) anomalies over the North Atlantic. There was a weak connection between large-scale maritime surface temperature anomalies and local precipitation and temperature variability over the European continent. The decadal forecasts for time scale of nine years and longer, on the other hand, exhibited moderate skill. The dependency between temporal and spatial scales was found to differ for the temperature and the mean SLP anomalies, but we found little indication that the decadal predictions for anomalies with large-scale regional extent were associated higher skill than for more local patterns in these forecasts.

Keywords

• model output statistics
• downscaling
• decadal predictions
• common eofs