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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Shellby Miller
Administrative Professional II
School of Earth and Atmospheric Sciences
Georgia Institute of Technology
Environmental Science and Technology Building
Email: smiller94@gatech.edu
Summary Sentence: Can we predict El Niño? Advances and Challenges
Full Summary: No summary paragraph submitted.
Seminar: Dr. Matt NewmanDr. Matthew Newman, Senior Research Scientist at NOAA in Boulder, CO
Research Interests:
Dr. Matthew Newman studies climate prediction and predictability on time scales ranging from weekly to decadal, with an emphasis on the use and diagnosis of empirical models constructed from both observations and the output of global climate models.
ABSTRACT
Seasonal forecasts made by coupled general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields an ensemble forecast, without additional model integration. This technique is applied to four of the eight CGCMs comprising the multi-model ensemble used operationally by NCEP, by selecting from prior long control runs those model states whose monthly tropical IndoPacific sea surface temperature and height anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1-24 months during 1982-2009. Deterministic and probabilistic skill measures of these model-analog hindcasts are comparable to, and in the ENSO region better than, the initialized CGCM hindcasts, for both the individual models and the multi-model ensemble. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations may offer skillful seasonal to interannual forecasts of tropical IndoPacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could allow anyone with a laptop and access to the IPCC database the ability to make their own skillful ENSO forecasts.
If you are interested in talking with Dr. Newman, please contact Shellby Miller.
