Improved Combination of Multiple Atmospheric GCM Ensembles for Seasonal Prediction

By Andrew Robertson, Upmanu Lall, S. Zebiak, L. Goddard. 2004

162 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/748c5a6c33d26b76d43d9fa820db2fa26cb73c5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Michael K. Tippett
2. Reto Knutti
3. T. Delsole
4. A. Barnston
5. C. Tebaldi
6. M. Dilley
7. L. Goddard
8. A. Simon

In Global and Planetary Change

1. Michael K. Tippett
2. Reto Knutti
3. T. Delsole
4. A. Barnston
5. C. Tebaldi
6. M. Dilley
7. L. Goddard
8. A. Simon

Citations in the corpus

• A Bayesian Framework for Multimodel Regression (2007, background): “(1979), and Smith and Campbell (1980). A fully Bayesian approach would assign prior distributions to the covariance matrices I and 2 .”
• The use of the multi-model ensemble in probabilistic climate projections (2007, methodology): “In many cases, Bayesian methods (e.g. Robertson et al. 2004) or weighted averages, where weights are determined by using the historical relationship between forecasts and observations (e.g. Krishnamurti et al. 2000), perform better than simple averages where each model is weighted equally.”
• El Niño: Catastrophe or Opportunity (2005, methodology): “…measures, such as the ranked probability skill score (RPSS) and reliability, the IRI’s Bayesian approach outperforms the individual models and also outperforms simpler model combination strategies, such as the straight pooling of all ensemble members (Barnston et al. 2003; Robertson et al. 2004).”
• A verification framework for interannual-to-decadal predictions experiments (2012, unknown): “…still face a relatively short history in face of decadal-scale variability Finally, it is possible that gains in prediction quality may be made by multi-model ensemble approaches (e.g. Hagedorn et al. 2005), as has been the case for seasonal prediction (Robertson et al. 2004; Graham et al. 2011).”
• A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes (2016, methodology): “MULTIMODEL SUPERENSEMBLE FORECASTS 358 the National Center for Atmospheric Research (NCAR) community model, the Australian model Predictive Ocean Atmosphere Model for Australia (POAMA), and four versions of FSU global coupled ocean atmosphere models by Krishnamurti et al. [2006a].”
• Skill of Multimodel ENSO Probability Forecasts (2008, background): “Then the resulting probabilities are averaged similar to Robertson et al. (2004).”