IMPROVING SEASONAL PREDICTION PRACTICES THROUGH ATTRIBUTION OF CLIMATE VARIABILITY

Anthony G. Barnston
Search for other papers by Anthony G. Barnston in
Current site
Google Scholar
PubMed
Close
,
Arun Kumar
Search for other papers by Arun Kumar in
Current site
Google Scholar
PubMed
Close
,
Lisa Goddard
Search for other papers by Lisa Goddard in
Current site
Google Scholar
PubMed
Close
, and
Martin P. Hoerling
Search for other papers by Martin P. Hoerling in
Current site
Google Scholar
PubMed
Close
Full access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

The Seasonal Diagnostics Consortium of the Applied Research Centers is engaging in a real-time activity to detect and understand the role of sea surface temperature (SST) anomalies in observed climate anomalies. The activity is aimed to improve practices in seasonal climate forecasting by fully harvesting the accumulated research evidence of the climate's sensitivity to ocean forcing. The approach, in the first phase of the activity, involves performing ensembles of atmospheric general circulation models (AGCMs) at several institutions, using the most recently observed global SST anomalies as prescribed forcings. The runs are routinely updated each month as the latest SST observations become available, adding to the archive of historical simulations spanning the last half-century.

The SST-forced signal in the seasonal mean climate is detected through the agreement among ensemble mean anomalies drawn from the simulations of the various AGCMs. The consortium activity also compares the dynamically forced signals with those estimated empirically, based on the observational archive. A comparison of the coordinated simulations with the observed climate anomalies is then made for two principal reasons: 1) to offer an attribution for the ocean's role in the origin of the observed seasonal climate anomalies, and 2) to determine the causes for success or failure of operational seasonal climate predictions, whose tools may be either mainly dynamically or empirically derived. It is expected that routine climate diagnostics and attribution efforts for climate anomalies will help further develop the knowledge base for improving the practice of seasonal climate predictions, and advance understanding of global climate on seasonal to decadal time scales.

International Research Institute for Climate Prediction, Columbia University, Palisades, New York

NOAA/NCEP/Climate Prediction Center, Washington, D.C.

NOAA/Climate Diagnostics Center, Boulder, Colorado

CORRESPONDING AUTHOR: Dr. Anthony G. Barnston, International Research Institute for Climate Prediction, Monell 227, 61 Rte. 9W, Palisades, NY 10964, E-mail: tonyb@iri.columbia.edu

The Seasonal Diagnostics Consortium of the Applied Research Centers is engaging in a real-time activity to detect and understand the role of sea surface temperature (SST) anomalies in observed climate anomalies. The activity is aimed to improve practices in seasonal climate forecasting by fully harvesting the accumulated research evidence of the climate's sensitivity to ocean forcing. The approach, in the first phase of the activity, involves performing ensembles of atmospheric general circulation models (AGCMs) at several institutions, using the most recently observed global SST anomalies as prescribed forcings. The runs are routinely updated each month as the latest SST observations become available, adding to the archive of historical simulations spanning the last half-century.

The SST-forced signal in the seasonal mean climate is detected through the agreement among ensemble mean anomalies drawn from the simulations of the various AGCMs. The consortium activity also compares the dynamically forced signals with those estimated empirically, based on the observational archive. A comparison of the coordinated simulations with the observed climate anomalies is then made for two principal reasons: 1) to offer an attribution for the ocean's role in the origin of the observed seasonal climate anomalies, and 2) to determine the causes for success or failure of operational seasonal climate predictions, whose tools may be either mainly dynamically or empirically derived. It is expected that routine climate diagnostics and attribution efforts for climate anomalies will help further develop the knowledge base for improving the practice of seasonal climate predictions, and advance understanding of global climate on seasonal to decadal time scales.

International Research Institute for Climate Prediction, Columbia University, Palisades, New York

NOAA/NCEP/Climate Prediction Center, Washington, D.C.

NOAA/Climate Diagnostics Center, Boulder, Colorado

CORRESPONDING AUTHOR: Dr. Anthony G. Barnston, International Research Institute for Climate Prediction, Monell 227, 61 Rte. 9W, Palisades, NY 10964, E-mail: tonyb@iri.columbia.edu
Save