%0 Journal Article %1 Arribas2010GloSea4 %A Arribas, Alberto %A Glover, M. %A Maidens, A. %A Peterson, K. %A Gordon, M. %A MacLachlan, C. %A Graham, R. %A Fereday, D. %A Camp, J. %A Scaife, A. A. %A Xavier, P. %A McLean, P. %A Colman, A. %A Cusack, S. %B Monthly Weather Review %D 2010 %I American Meteorological Society %J Mon. Wea. Rev. %K euporias %N 6 %P 1891--1910 %R 10.1175/2010mwr3615.1 %T The GloSea4 Ensemble Prediction System for Seasonal Forecasting %U http://dx.doi.org/10.1175/2010mwr3615.1 %V 139 %X AbstractSeasonal forecasting systems, and related systems for decadal prediction, are crucial in the development of adaptation strategies to climate change. However, despite important achievements in this area in the last 10 years, significant levels of skill are only generally found over regions strongly connected with the El Niño?Southern Oscillation. With the aim of improving the skill of regional climate predictions in tropical and extratropical regions from intraseasonal to interannual time scales, a new Met Office global seasonal forecasting system (GloSea4) has been developed. This new system has been designed to be flexible and easy to upgrade so it can be fully integrated within the Met Office model development infrastructure. Overall, the analysis here shows an improvement of GloSea4 when compared to its predecessor. However, there are exceptions, such as the increased model biases that contribute to degrade the skill of Niño-3.4 SST forecasts starting in November. Global ENSO teleconnections and Madden?Julian oscillation anomalies are well represented in GloSea4. Remote forcings of the North Atlantic Oscillation by ENSO and the quasi-biennial oscillation are captured albeit the anomalies are weaker than those found in observations. Hindcast length issues and their implications for seasonal forecasting are also discussed.

@article{Arribas2010GloSea4, abstract = {{AbstractSeasonal} forecasting systems, and related systems for decadal prediction, are crucial in the development of adaptation strategies to climate change. However, despite important achievements in this area in the last 10 years, significant levels of skill are only generally found over regions strongly connected with the El {Ni\~{n}o?Southern} Oscillation. With the aim of improving the skill of regional climate predictions in tropical and extratropical regions from intraseasonal to interannual time scales, a new Met Office global seasonal forecasting system ({GloSea4}) has been developed. This new system has been designed to be flexible and easy to upgrade so it can be fully integrated within the Met Office model development infrastructure. Overall, the analysis here shows an improvement of {GloSea4} when compared to its predecessor. However, there are exceptions, such as the increased model biases that contribute to degrade the skill of Ni\~{n}o-3.4 {SST} forecasts starting in November. Global {ENSO} teleconnections and {Madden?Julian} oscillation anomalies are well represented in {GloSea4}. Remote forcings of the North Atlantic Oscillation by {ENSO} and the quasi-biennial oscillation are captured albeit the anomalies are weaker than those found in observations. Hindcast length issues and their implications for seasonal forecasting are also discussed.}, added-at = {2019-08-14T18:35:35.000+0200}, author = {Arribas, Alberto and Glover, M. and Maidens, A. and Peterson, K. and Gordon, M. and MacLachlan, C. and Graham, R. and Fereday, D. and Camp, J. and Scaife, A. A. and Xavier, P. and McLean, P. and Colman, A. and Cusack, S.}, biburl = {https://www.bibsonomy.org/bibtex/28b04cf7db0f405d3e62d47e356102f50/karinawilliams}, booktitle = {Monthly Weather Review}, citeulike-article-id = {9897414}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/2010MWR3615.1}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/2010mwr3615.1}, day = 7, doi = {10.1175/2010mwr3615.1}, interhash = {5cd17e3c8e880e9aadabe5e9bf3c1ab2}, intrahash = {8b04cf7db0f405d3e62d47e356102f50}, journal = {Mon. Wea. Rev.}, keywords = {euporias}, month = dec, number = 6, pages = {1891--1910}, posted-at = {2012-10-18 14:08:33}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2019-08-14T18:35:35.000+0200}, title = {The {GloSea4} Ensemble Prediction System for Seasonal Forecasting}, url = {http://dx.doi.org/10.1175/2010mwr3615.1}, volume = 139, year = 2010 }