A multiple model assessment of seasonal climate forecast skill for applications

Research output: Contribution to journalArticlepeer-review

Authors

  • David Lavers
  • Lifeng Luo
  • Eric F. Wood

Colleges, School and Institutes

Abstract

Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere-land-ocean models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast for applications. Results suggest there is a deficiency of skill in the forecasts beyond month-1, with precipitation having a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also be seen as a benchmark of current climate prediction capability using (dynamic) couple models.

Details

Original languageEnglish
Article numberL23711
Number of pages6
JournalGeophysical Research Letters
Volume36
Issue number23
Publication statusPublished - 15 Dec 2009