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Performance-based Probabilistic Multi-Model Climate Change Scenarios

PI: Lisa Goddard
Institution: Columbia University
Additional Investigators: Arthur Greene
In the Third Assessment Report of the IPCC climate change scenarios with credible uncertainty estimates are available only globally or for large-scale patterns/footprints. Regional change scenarios exist, but they are based on multi-model agreement, at best, rather than some estimate of model skill. The current inability to provide a strong basis for evaluating scenario credibility at the regional level constitutes a fairly serious methodological gap, which this research purposes to fill. The probabilistic climate change scenarios will address several of the priorities of the IPCC research, including examination of changes in interannual variability as well as long-term trends and how those changes manifest themselves at the regional scale. The work builds on existing methodologies used at the International Research Institute for Climate Prediction (IRI) for verifying dynamical models and constructing probabilistic seasonal-to-interannual climate forecasts. The work consists of two main parts:

* Verification of the performance of 20th Century temporal characteristics, such as the trends in multi-decadal means and the interannual variability about those means, from the AOGCMs used for 21st Century climate change predictions; and,

* Construction of probabilistic multi-model scenarios for 21st Century climate and its variability, using the results from (1) as an objective basis for assigning weights to the model predictions.

The analyses will be performed spatially from the grid scale to the regional scale for near-surface air temperature and precipitation. The required data are monthly-mean precipitation and near-surface air temperature from the AOGCMs that are producing ensemble runs.

The methods employed in this research, applying techniques of multi-model ensembling that are based on model performance, have not yet been applied in the context of climate change predictions and can pilot further research for future IPCC reports. The findings of this research also can be used to set the longer-range context for seasonal-to-interannual climate variability and predictions. The suggestion here is that proper synthesis of seasonal forecasts, and longer-term assessments, taking into account the uncertainties of each, provides the best opportunity to minimize losses, take advantage of opportunity, and work toward sustainable practices.
  • Greene, A.M., L. Goddard and U. Lall, 2006: Probabilistic multimodel regional temperature change projections. Journal of Climate, 19, 4326-4343, 10.1175/JCLI3864.1. Abstract. Full Article. Edit.

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