High predictive skill of global surface temperature a year ahead

• Published in: Geophysical Research Letters
• Main Authors: Folland, C.K., Colman, A.W., Smith, D.M., Boucher, Olivier, Parker, D.E., Vernier, J.-P.
• Other Authors: Met Office Hadley Centre (MOHC), United Kingdom Met Office Exeter, Department of Earth Sciences Gothenburg, Göteborgs Universitet = University of Gothenburg (GU), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL), Science Systems and Applications, Inc. Hampton (SSAI), NASA Langley Research Center Hampton (LaRC)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2013
• Subjects: [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology; Arctic
• Online Access: https://hal.science/hal-01092299; https://hal.science/hal-01092299/document; https://hal.science/hal-01092299/file/folland_et_al_GRL_2013.pdf; https://doi.org/10.1002/grl.50169

International audience 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. Key Points High skill of predictions, 2000-2011 Even higher skill is potentially possible Coupled model hindcasts have at least this skill over same period ©2013. American Geophysical Union. All Rights Reserved.