%0 Journal Article %1 Folland2013High %A Folland, Chris K. %A Colman, Andrew W. %A Smith, Doug M. %A Boucher, Olivier %A Parker, David E. %A Vernier, Jean-Paul %D 2013 %J Geophys. Res. Lett. %K temperature colleagues decadal skill %N 4 %P 761--767 %R 10.1002/grl.50169 %T High predictive skill of global surface temperature a year ahead %U http://dx.doi.org/10.1002/grl.50169 %V 40 %X We discuss 13 real-time forecasts of global annual-mean surface temperature issued by the United Kingdom Met Office for 1 year ahead for 2000–2012. These involve statistical, and since 2008, initialized dynamical forecasts using the Met Office DePreSys system. For the period when the statistical forecast system changed little, 2000–2010, issued forecasts had a high correlation of 0.74 with observations and a root mean square error of 0.07°C. However, the HadCRUT data sets against which issued forecasts were verified were biased slightly cold, especially from 2004, because of data gaps in the strongly warming Arctic. This observational cold bias was mainly responsible for a statistically significant warm bias in the 2000–2010 forecasts of 0.06°C. Climate forcing data sets used in the statistical method, and verification data, have recently been modified, increasing hindcast correlation skill to 0.80 with no significant bias. Dynamical hindcasts for 2000–2011 have a similar correlation skill of 0.78 and skillfully hindcast annual mean spatial global surface temperature patterns. Such skill indicates that we have a good understanding of the main factors influencing global mean surface temperature.

@article{Folland2013High, abstract = {We discuss 13 real-time forecasts of global annual-mean surface temperature issued by the United Kingdom Met Office for 1 year ahead for 2000–2012. These involve statistical, and since 2008, initialized dynamical forecasts using the Met Office DePreSys system. For the period when the statistical forecast system changed little, 2000–2010, issued forecasts had a high correlation of 0.74 with observations and a root mean square error of 0.07°C. However, the HadCRUT data sets against which issued forecasts were verified were biased slightly cold, especially from 2004, because of data gaps in the strongly warming Arctic. This observational cold bias was mainly responsible for a statistically significant warm bias in the 2000–2010 forecasts of 0.06°C. Climate forcing data sets used in the statistical method, and verification data, have recently been modified, increasing hindcast correlation skill to 0.80 with no significant bias. Dynamical hindcasts for 2000–2011 have a similar correlation skill of 0.78 and skillfully hindcast annual mean spatial global surface temperature patterns. Such skill indicates that we have a good understanding of the main factors influencing global mean surface temperature.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Folland, Chris K. and Colman, Andrew W. and Smith, Doug M. and Boucher, Olivier and Parker, David E. and Vernier, Jean-Paul}, biburl = {https://www.bibsonomy.org/bibtex/2d5d85edfab757c536e7d6e30137737ba/pbett}, citeulike-article-id = {12938391}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/grl.50169}, day = 28, doi = {10.1002/grl.50169}, interhash = {8a8ea4c131d928d98d9ca3bf5e55baf2}, intrahash = {d5d85edfab757c536e7d6e30137737ba}, journal = {Geophys. Res. Lett.}, keywords = {temperature colleagues decadal skill}, month = feb, number = 4, pages = {761--767}, posted-at = {2014-02-17 21:50:19}, priority = {2}, timestamp = {2018-06-22T18:34:09.000+0200}, title = {High predictive skill of global surface temperature a year ahead}, url = {http://dx.doi.org/10.1002/grl.50169}, volume = 40, year = 2013 }