IMPROVING SEASONAL PREDICTION PRACTICES THROUGH ATTRIBUTION OF CLIMATE VARIABILITY

IMPROVING SEASONAL PREDICTION PRACTICES THROUGH ATTRIBUTION OF CLIMATE VARIABILITY 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

IMPROVING SEASONAL PREDICTION PRACTICES THROUGH ATTRIBUTION OF CLIMATE VARIABILITY

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
D.O.I.
10.1175/BAMS-86-1-59
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: Jan 4, 2005

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